Tokushima University / Educator and Researcher Directory --- Matsumoto, Kazuyuki

Personal Web Page

https://www-a2.is.tokushima-u.ac.jp/research

https://researchmap.jp/read0156851 (researchmap)

https://orcid.org/0000-0002-9820-1470 (ORCID)

Field of Study

○	Affective Computing, Natural Language Processing

Subject of Study

○	人間の発話文からの感情認識とロボットの感情創生に関する研究, 機械翻訳技術を応用した英作文支援システムの開発 (Emotion Recognition, English Composition Support, Outcome Management)

Book / Paper

Book:

1.	Kazuyuki Matsumoto and Haruto UDA : 小規模テキストデータの前処理技術:データ拡張，リサンプリング，欠損値補完, Technical Information Institute Co., Ltd., Oct. 2024.
2.	Kazuyuki Matsumoto and 土屋誠司 : 情報デザインとコミュニケーション, Sogensha,inc., Jul. 2023. (Summary) 【目次】 1章情報デザイン情報デザインの役割情報の抽象化情報伝達の方法 2章人とコンピュータコンピュータを用いた問題解決シミュレーションモデル化アクセシビリティ技術情報の管理 3章情報の表現方法情報の取得と共有色や形で表すグラフィックツール新しい情報表現技法 4章情報の伝達方法情報伝達手段とツール UIとUX UI・UXの設計と評価 5章コミュニケーションコミュニケーションの形式とツールオンラインコミュニケーションの特性コンテンツ分析と多メディアの融合 6章コミュニケーションツール SNS 遠隔会議システムやチャットツール対面/バーチャルのハイブリッド 7章コンテンツの作成と改善コンテンツの作成コンセプトデザインとプロトタイプ 8章コンテンツの発信と評価コンテンツの発信コンテンツ配信後の注意点コンテンツの評価と改善 9章情報デザインの未来社会に開かれた情報デザイン AIの台頭と情報デザイン未来の情報デザイン (Keyword) 情報デザイン / アクセシビリティ技術 / UI,UX / コンテンツ分析 / コンセプトデザイン
3.	Kazuyuki Matsumoto : Information Systems -Intelligent Information Processing Systems, Natural Language Processing, Affective Computing and Artificial Intelligence, and an Attempt to Build a Conversational Nursing Robot-, London, Oct. 2021. (Keyword) Affective Computing / artificial intelligence / Nursing Robot (Link to Publication Site) ● Publication site (DOI): 10.5772/intechopen.87694 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.5772/intechopen.87694 (DOI: 10.5772/intechopen.87694)
4.	Kenji Kita, Kazuyuki Matsumoto, Minoru Yoshida, Masami Shishibori and Masaki Oono : Hands-on Data Preprocessing in AI and Machine Learning, --- Data Science with Python ---, Kagakujyoho Shuppan Co., Ltd., Tokyo, Aug. 2021. (Summary) Research on artificial intelligence (AI) itself has been going on since the dawn of computing, with the "first AI boom" in the late 1950s and 1960s, the "second AI boom" in the 1980s and 1990s, and now we are in the midst of the "third AI boom". The major difference between this third boom and the previous first and second booms is the emergence of intelligent information processing systems that approach human capabilities in many fields. This can be attributed to a variety of factors, but the most significant are the accumulation of various kinds of big data, the accelerated progress of IT technologies that make such big data available, and the emergence of new machine learning paradigms such as deep learning. One of the keys to the success of AI and machine learning systems is the pre-processing of data. The essence of machine learning is to correctly make predictions and inferences about unknown data by learning the structure, regularities, and universality that lie behind large amounts of data. However, if the quality of the data used for learning is poor, the system will not be able to learn correctly, and the resulting system will not be accurate. In order to build a highly accurate system, it is important to eliminate poor data and process the data to convert it into a form that is easy to learn. This is exactly what preprocessing is responsible for. In extreme cases, it can be said that the success or failure of preprocessing guarantees the quality of the entire machine learning system. According to one theory, in the field of AI and machine learning system development, 60 to 80% of the time spent by engineers is spent on data collection and preprocessing. For efficient system development, it is essential to master preprocessing techniques. This book specifically focuses on preprocessing techniques, which have not been sufficiently covered in conventional books on machine learning and data science, and aims to be a practical book that allows readers to understand the techniques not only by explaining them, but also by actually running programs. The sister volume of this book, "Fundamentals," introduces basic preprocessing techniques. This book, "Applications," explains more advanced preprocessing techniques and preprocessing techniques for media data such as text, images, sound, and music. (Keyword) artificial intelligence / machine learning / Python / Preprocessing / data analysis / data mining / Google Colab / Big Data / Feature Selection / Dimensionality Reduction / NumPy / pandas / scikit-learn / matplotlib / TensorFlow / Keras / Data Cleaning / Feature Extraction / Scaling / Standardization / normalization / Normalization / Data Cleansing / Data Augmentation / Binning / Discretization / Categorical Data / Category Data / One-hot Encoding / One-hot Vector / Label Encoding / Frequency Encoding / Count Encoding / Ranked Frequency Encoding / Label-count Encoding / Feature Hashing / Global Hashing Space / Per-field Hashing Space / MurmurHash3 / Entity Embedding / Distributed Representation / Embedding / Table2Vec / Imbalanced Data / Resampling / Undersampling / Downsampling / Oversampling / Upsampling / Random Oversampling / Random Undersampling / ENN / SMOTE / ADASYN / Time-series Data / Point Process / Windowed Statistics / Aozora Bunko / Word Segmentation / morphological analysis / MeCab / Bigram / Trigram / N-gram / SentencePiece / Stop Word / Stop Word List / vector space model / Bag-of-Words / tf-idf / gensim / Topic Model / LDA / Gibbs Sampling / word2vec / Skip-gram / Continuous Bag-of-Words / Negative Sampling / Hierarchical Softmax / CNN / Forward Propagation / Activation Function / Back Propagation / Sum of Squares Error / Cross Entropy / Epoch / Gradient Descent / Batch Learning / Online Learning / Mini-batch Learning / iteration / Convolution Layer / Pooling Layer / Fully Connected Layer / Feature Map / Zero Padding / Maximum Pooling / Average Pooling / Dropout Layer / SDG / Shear Mapping / Fine-tuning / VGG16 / Sampling Rate / Quantization Bit Rate / Cocktail-party Effect / Spectral Subtraction / Harmonic/Percussive Sound Separation / Short-time Fourier Transform / Inverse Short-time Fourier Transform / Wiener Filter / Audio Source Separation
5.	Kenji Kita, Ryota Nishimura and Kazuyuki Matsumoto : Introduction to Data Preprocessing in AI and Machine Learning, --- Getting Started with Python ---, Kagakujyoho Shuppan Co., Ltd., Tokyo, Jun. 2021. (Summary) Around 2010, the term "Big Data" began to rapidly permeate the world around us. At the same time, the construction of intelligent AI systems based on machine learning from big data has even become a boom these days. This book focuses on the pre-processing of data, which is an inevitable part of building such machine learning systems, and although the primary focus is on machine learning systems such as AI, the various techniques described in this book can also be used effectively in data mining and data analysis to acquire useful knowledge from big data. However, the various techniques described in this book can also be used effectively in data mining and data analysis to obtain useful knowledge from big data.The term "big data" is pleasant to the ears and may sound like a treasure trove, but in fact big data is a mixture of cobbles and stones, a mixture of treasure and garbage. In fact, big data is a mixture of treasure and trash, and the larger the scale of the data, the more trash there is, and the more difficult it becomes to extract the treasure from it. In the field of computer science, there is a saying, "Garbage In, Garbage Out" (or GIGO for short). To build a good machine learning system, it is important to eliminate garbage from data and process the data into a form that is easy to learn, which is the essence of preprocessing.Recently, many frameworks and libraries for machine learning such as TensorFlow and Keras are available free of charge, and using these frameworks and libraries may seem to lower the threshold for programming. Using publicly available datasets for machine learning, anyone can easily create an AI system. However, there is a big problem when building your own system using your own data set. As is often said, real-world data is dirty. If you use the data as it is, that is exactly what you will get.If we use the data as it is, we will end up in a "Garbage In, Garbage Out" situation. Whether or not a useful AI system can be built from real data depends on the success of pre-processing. According to one theory, in the actual construction of AI and machine learning systems, 60-80% of the time spent by engineers is spent on data collection and preprocessing.This book focuses on preprocessing techniques, which have not been sufficiently covered in conventional books on machine learning, and aims to be a practical book where readers can understand the techniques not only by explaining them but also by actually running programs. This book has a sister volume, "Practical Edition", which is planned to be published in the near future. In "Practical Edition", more advanced preprocessing techniques and preprocessing techniques for media data such as text, images, sound, and music are explained. (Keyword) artificial intelligence / machine learning / Python / Preprocessing / data analysis / data mining / Google Colab / Big Data / Feature Selection / Dimensionality Reduction / NumPy / pandas / scikit-learn / matplotlib / TensorFlow / Keras / Data Cleaning / Feature Extraction / Scaling / Standardization / normalization / Normalization / Data Cleansing / Data Augmentation / Binning / Discretization / Outlier / IQR / Boxplot / Smirnov-Grubbs' test / Missing Value / List-wise Case Deletion / Pair-wise Case deletion / Single Imputation / Multiple Imputation / Filter Method / Chi-squared Test / analysis of variance / Pearson Correlation Coefficient / PCA / NMF / CCA / LDA / Kernel PCA / t-SNE / UMAP / MDS / self-organizing map / autoencoder / SMOTE
6.	Kazuyuki Matsumoto : Utterance Emotion Estimation by Using Feature of Syntactic Pattern, IntechOpen, Mar. 2021. (Summary) Emotion has been defined as basic emotions by various researchers, however, there are not many studies describing the relation between emotion and language patterns in detail based on statistical information. There are various languages allover the world, and even a language of the same country has different writing styles/expressions depending on which language media is used or who is a writer/speaker, which is thought to make it difficult to analyze the relation of emotion andlanguage patterns. The author has been engaged in constructing and analyzing emotion corpora in some domains based on different sources. From the analysis results, emotion expressions started to become more understood that they havedifferences and tendencies according to the attributes of the writers and the speakers. In this chapter, I focused on the differences detected in the attributes of the writer/speaker with respect to language patterns; in usage tendencies or combinations of words, unknown expressions (slangs), sentence patterns, non-verbal expressions (emoji, emoticon, etc.) with relevant emotions, then introduce the outcome of the analytical survey on a large scale corpus obtained from a socialnetworking service. (Keyword) emotion estimation / kansei robotics / slang / emoji (Link to Publication Site) ● Publication site (DOI): 10.5772/intechopen.96597 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.5772/intechopen.96597 (DOI: 10.5772/intechopen.96597)
7.	Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Analysis of Information Spreading by Social Media based on Emotion and Empathy, InTechOpen, Sep. 2019. (Summary) The number of social media users has increased exponentially in recent times,and various types of social media platforms are being introduced. While socialmedia has become a convenient communication tool, its use has caused varioussocial problems. Some users who cannot imagine the emotions their posts mayinduce in readers cause what is termed as the flaming phenomenon. In somecases, users intentionally repeat strong remarks for self-advertisement. To identifythe cause of this phenomenon, it is necessary to analyze the posted contents or thepersonalities of the users who cause the flaming. However, it is difficult to reach ageneralized conclusion because each case varies depending on the circumstancesand individual. In this chapter, we study the phenomenon of information spreadingvia communication on social media by conducting a detailed analysis of replies andnumber of retweets in Japanese, and we reveal the relation between the feedback onsuch posts and the emotions or empathy they result in. (Keyword) social media / information diffusion / flaming / buzz / Twitter (Link to Publication Site) ● Publication site (DOI): 10.5772/intechopen.88886 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.5772/intechopen.88886 (DOI: 10.5772/intechopen.88886)
8.	Fuji Ren and Kazuyuki Matsumoto : 言語・音声・顔表情・脳波を総合利用した感情測定システム, CMC Publishing Co.,Ltd., Aug. 2019. (Keyword) multi modal sensing / emotion measurement system / voice / language
9.	Yoshiyasu Ishimaru, Shin-ichi Ito, Takeshi Uno, Yasuhiro Okamura, Kenji Ohira, Kosuke Ono, Hidekazu Kakei, Kazuhide Kanenishi, Maiko Sawada, Toru Hasunuma, Ray S. Furuya, Kenji Matsuura, Kazuyuki Matsumoto, 土岐順子, 阿部香織, 山﨑紅, 辰巳丈夫 and 勝村幸博 : 情報科学入門, 日経BP社, Tokyo, Apr. 2019.
10.	Yoshiyasu Ishimaru, Shin-ichi Ito, Takeshi Uno, Yasuhiro Okamura, Kenji Ohira, Kosuke Ono, Hidekazu Kakei, Kazuhide Kanenishi, Maiko Sawada, Toru Hasunuma, Ray S. Furuya, Kenji Matsuura, Kazuyuki Matsumoto, 土岐順子, 阿部香織, 山﨑紅, 辰巳丈夫 and 勝村幸博 : 情報科学入門, 日経BP社, Tokyo, Apr. 2018.
11.	Kazuyuki Matsumoto, Hayato Shimizu, Minoru Yoshida and Kenji Kita : Time-Series Analysis of Video Comments on Social Media, Worlds largest Science, Technology & Medicine Open Access book publisher, Jul. 2017. (Summary) In this study, we propose a method to detect unfair rating cheat caused by multiple comment postings focusing on time-series analysis of the number of comments. We defined the videos that obtained a lot of comments by unfair cheat as unfair video and defined the videos which obtained without unfair cheat as popular video. Specifically, our proposed method focused on the difference of chronological distributions of the comments between the popular videos and the unfair videos. As the evaluation result, our proposed method could obtain higher accuracy than that of the baseline method. (Keyword) video comments / shared videos / comment analysis, / time-series analysis (Link to Publication Site) ● Publication site (DOI): 10.5772/intechopen.68636 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.5772/intechopen.68636 (DOI: 10.5772/intechopen.68636)
12.	Fuji Ren, Shun Nishide, Kazuyuki Matsumoto, XIN KANG, Duo Feng and Mengjia He : Artificial Intelligence with Uncertainty, NTS, Jun. 2017.
13.	Yoshiyasu Ishimaru, Shin-ichi Ito, Takeshi Uno, Yasuhiro Okamura, Kenji Ohira, Kosuke Ono, Hidekazu Kakei, Kazuhide Kanenishi, Maiko Sawada, Toru Hasunuma, Ray S. Furuya, Kenji Matsuura, Kazuyuki Matsumoto, 土岐順子, 阿部香織, 山﨑紅, 辰巳丈夫 and 勝村幸博 : 情報科学入門, 日経BP社, Tokyo, Apr. 2017.
14.	Kazuyuki Matsumoto : The Current State of Performance and Development of Natural Language Processing Required for Humanoid Caring Robots, Fukuro Shuppan Publishing, Mar. 2017. (Keyword) Empathic understanding / Nursing / Nurse-robot / Communication / Caring
15.	Fuji Ren and Kazuyuki Matsumoto : Natural Language Processing Capabilities Required for Humanoid Nursing Robots, Fukuro Shuppan Publishing, Mar. 2017.
16.	Kazuyuki Matsumoto, Fuji Ren, Minoru Yoshida and Kenji Kita : Refinement by Filtering Translation Candidates and Similarity Based Approach to Expand Emotion Tagged Corpus, Jan. 2017. (Summary) We attempted to expand corpus without translating target linguistic resource. The result of the evaluation experiment using the machine learning algorithm showed the effectiveness of the expanded emotion corpus based on the original languages unannotated sentences and their similar sentences. (Keyword) emotion tagged corpus / Japanese-English parallel corpora / emotion estimation (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-319-52758-1_15 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85011342735 (DOI: 10.1007/978-3-319-52758-1_15, Elsevier: Scopus)

Academic Paper (Judged Full Paper):

1.	Kiuchi Keita, XIN KANG, Ryota Nishimura, Sasayama Manabu and Kazuyuki Matsumoto : Predicting Physical and Mental Health Status One Month Later through Interview-Based Stress Assessment, Industrial Health, Vol.62, No.4, 237-251, (Summary) This study conducted an interview-based stress evaluation that considered the psychosocial models of work stress and verified the evaluations predictive validity. A four-stage assessment comprising a pre-survey, pre-interview questionnaire, stress assessment interview, and post-survey after one month was conducted with 50 Japanese workers. Additionally, 16 occupational health professionals provided stress evaluations based on recorded interview videos. Variables based on intraclass correlation coefficients (ICCs) were computed in multiple ways to compare the agreement among the evaluators. The generalized estimating equation (GEE) was conducted to evaluate the prediction models. The overall ICC among the evaluators was 0.58. The GEE revealed that the mean score of the evaluators in the interview-based stress evaluation significantly predicted psychological symptoms (β =2.02, p=0.019), burnout (β =0.77, p<0.001), and well-being (β =0.64, p=0.007) one month later, even after adjusting for the self-reported stress levels measured in the pre-survey. The predictive validity of the proposed interview-based stress evaluation was confirmed. Although there are several challenges in standardizing this evaluation, semi-structured interviews are an effective tool for understanding work stress. (Keyword) burnout / chronic stress / interview-based assessment / job stress / stress assessment / well-being (Link to Publication Site) ● Publication site (DOI): 10.2486/indhealth.2023-0144 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.2486/indhealth.2023-0144 (DOI: 10.2486/indhealth.2023-0144)
2.	Keita Kiuchi, Hidehiro Umehara, Koushi Irizawa, XIN KANG, Masahito Nakataki, Minoru Yoshida, Shusuke Numata and Kazuyuki Matsumoto : An Exploratory Study of the Potential of Online Counseling for University Students by a Human-Operated Avatar Counselor, Healthcare, Vol.12, No.1287, 2024. (Summary) Recently, the use of digital technologies, such as avatars and virtual reality, has been increasingly explored to address university students mental health issues. However, there is limited research on the advantages and disadvantages of counselors using avatars in online video counseling. Herein, 25 university students were enrolled in a pilot online counseling session with a human counselor-controlled avatar, and asked about their emotional experiences and impressions of the avatar and to provide qualitative feedback on their communication experience. Positive emotions during the session were associated with impressions of the avatars intelligence and likeability. The anthropomorphism, animacy, likeability, and intelligent impressions of the avatar were interrelated, indicating that the avatars smile and the counselors expertise in empathy and approval may have contributed to these impressions. However, no associations were observed between participant experiences and their prior communication with avatars, or between participant experiences and their gender or the perceived gender of the avatar. Accordingly, recommendations for future practice and research are provided. Accumulating practical and empirical findings on the effectiveness of human-operated avatar counselors is crucial for addressing university students mental health issues. (Keyword) avatar counseling / human-operated avatars / university students / digital mental health / empathy / communication / anthropomorphism / animacy / likeability / perceived intelligence (Link to Publication Site) ● Publication site (DOI): 10.3390/healthcare12131287 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 38998822 ● Summary page in Scopus @ Elsevier: 2-s2.0-85198398651 (DOI: 10.3390/healthcare12131287, PubMed: 38998822, Elsevier: Scopus)
3.	Keita Kiuchi, XIN KANG, Ryota Nishimura, Manabu Sasayama and Kazuyuki Matsumoto : Causal Effects of High Stress Assessed via Interviews on Mental and Physical Health: Toward Computer Agent-Driven Stress Assessment, Journal of Occupational and Environmental Medicine, 2024. (Summary) This study investigated the causal effect of high stress assessment via an interview on the mental and physical health of workers one month later. Stress assessment interviews and feedback were conducted with 50 Japanese workers. In addition to the interviewer, two occupational health professionals assessed participants' stress based on recordings. The average treatment effect was estimated by propensity score matching. High stress, according to the interview-based assessment, had a significant negative causal effect on self-reported well-being one month later (95% confidence interval: -3.02, -1.10). In addition, no effect of high stress on stress load, mental and physical symptoms, or burnout was observed. This study provides important insights into the prognosis of individuals who were assessed through interviews to have high stress. The findings are expected to help automate stress assessments using computer agents. (Link to Publication Site) ● Publication site (DOI): 10.1097/JOM.0000000000003117 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 38603579 ● Search Scopus @ Elsevier (PMID): 38603579 ● Search Scopus @ Elsevier (DOI): 10.1097/JOM.0000000000003117 (DOI: 10.1097/JOM.0000000000003117, PubMed: 38603579)
4.	Fuji Ren, Yangyang Zhou, Jiawen Deng, Kazuyuki Matsumoto, Duo Feng, Tianhao She, Ziyun Jiao, Zheng Liu, Taihao Li, Satoshi Nakagawa and XIN KANG : Tracking Emotions using an Evolutionary Model of Mental State Transitions: Introducing a New Paradigm, Intelligent Computing, 1-24, 2024. (Tokushima University Institutional Repository) ● Metadata: 119667 (Link to Publication Site) ● Publication site (DOI): 10.34133/icomputing.0075 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.34133/icomputing.0075 (Tokushima University Institutional Repository: 119667, DOI: 10.34133/icomputing.0075)
5.	Zipaer Mayinuer, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Using Machine Learning to Classify Information Related to Child Rearing of Infants from Twitter, Advances in Machine Learning & Artificial Intelligence, Vol.4, No.2, 32-40, 2023.
6.	Motonobu Yoshida, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : System to Correct Toxic Expression with BERT and to Determine the Effect of the Attention Value, Communications in Computer and Information Science, Vol.1842, 239-253, 2023. (Summary) This paper describes an extended experiment on a system that converts sentences with toxic expressions into safe sentences, along with the evaluation and influence of the system. In recent years, toxicity on social media has created many problems. We evaluated the effectiveness of the proposed system for identifying toxic sentences using a prediction model based on Bidirectional Encoder Representations from Transformers (BERT) and then converting them into safe sentences using attention values and a score that indicates whether the sentences are appropriate after the predictive conversion. Six patterns of methods were tested, with Pattern 6 being the most effective for mitigating toxicity. This pattern is a technique that changes the way to take the top sentences of a beam search for each number of treatments, in addition to converting words with an attention value above a threshold and their adjacent words and phrases and words registered in the toxic dictionary. We used multiple indicators to judge the effectiveness of this method and evaluated its ability to make the text safe while preserving its meaning. (Keyword) Toxic expression / BERT / classification / Text correction / Attention value (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-031-43471-6_11 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85174230206 (DOI: 10.1007/978-3-031-43471-6_11, Elsevier: Scopus)
7.	Hidetoshi Nakao, Msakazu Imaoka, Mitsumasa Hida, Ryota Imai, Misa Nakamura, Kazuyuki Matsumoto and Kenji Kita : Determination of individual factors associated with hallux valgus using SVM-RFE, BMC Musculoskeletal Disorders, Vol.24, No.1, 534, 2023. (Summary) Introduction This cross-sectional study aimed to determine the factors related to hallux valgus (HV) and their importance using support vector machine-recursive feature elimination (SVM-RFE). Methods A total of 864 participants aged 18 years were enrolled. The Manchester scale was used to determine the presence of HV (summed scores for both feet 4). The questionnaire included items such as age, sex, height, weight, and foot measurements. These internal factors were analyzed to determine if they are related to HV using SVM-RFE. Results The results of tenfold cross-validation using SVM-RFE revealed that the numbers of feature selections were 10, 10, and 9 for age, sex, and body weight, respectively, and these factors were shown to be related to HV. HV was found to be more common in women than in men (women, 24.9%; men, 7.6%), but the sex difference was not significant in older people. Conclusion Age and sex were found to be important factors associated with HV identified via feature selection using SVM-RFE. (Keyword) feature selection / Hallux Valgus / Manchester Scale / SVM-RFE (Tokushima University Institutional Repository) ● Metadata: 118876 (Link to Publication Site) ● Publication site (DOI): 10.1186/s12891-023-06303-2 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 37386376 ● Summary page in Scopus @ Elsevier: 2-s2.0-85163790710 (Tokushima University Institutional Repository: 118876, DOI: 10.1186/s12891-023-06303-2, PubMed: 37386376, Elsevier: Scopus)
8.	Toshiya Akiyama, Kazuyuki Matsumoto, Kyoko Osaka, Ryuichi Tanioka, Feni Betriana, Yueren Zhao, Yoshihiro Kai, Misao Miyagawa, Yuko Yasuhara, Hirokazu Ito, Gil Soriano and Tetsuya Tanioka : Comparison of Subjective Facial Emotion Recognition and Facial Emotion Recognition Based on Multi-Task Cascaded Convolutional Network Face Detection between Patients with Schizophrenia and Healthy Participants, Healthcare, Vol.10, No.12, 2363, 2022. (Keyword) facial emotion recognition / humanrobot interaction / multi-task cascaded convolutional networks / reliability / patients with schizophrenia / healthy participants (Tokushima University Institutional Repository) ● Metadata: 118009 (Link to Publication Site) ● Publication site (DOI): 10.3390/healthcare10122363 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3390/healthcare10122363 (Tokushima University Institutional Repository: 118009, DOI: 10.3390/healthcare10122363)
9.	Kazuyuki Matsumoto, Reishi Amitani, Minoru Yoshida and Kenji Kita : Trend Prediction Based on Multi-Modal Affective Analysis from Social Networking Posts, Electronics, Vol.11, No.21, 2022. (Summary) This paper propose a method to predict the stage of buzz-trend generation by analyzing the emotional information posted on social networking services for multimodal information, such as posted text and attached images, based on the content of the posts. The proposed method can analyze the diffusion scale from various angles, using only the information at the time of posting, when predicting in advance and the information of time error, when used for posterior analysis. Specifically, tweets and reply tweets were converted into vectors using the BERT general-purpose language model that was trained in advance, and the attached images were converted into feature vectors using a trained neural network model for image recognition. In addition, to analyze the emotional information of the posted content, we used a proprietary emotional analysis model to estimate emotions from tweets, reply tweets, and image features, which were then added to the input as emotional features. The results of the evaluation experiments showed that the proposed method, which added linguistic features (BERT vectors) and image features to tweets, achieved higher performance than the method using only a single feature. Although we could not observe the effectiveness of the emotional features, the more emotions a tweet and its reply match had, the more empathy action occurred and the larger the like and RT values tended to be, which could ultimately increase the likelihood of a tweet going viral. (Keyword) multi-modal buzz prediction / information diffusion / affective analysis (Tokushima University Institutional Repository) ● Metadata: 118006 (Link to Publication Site) ● Publication site (DOI): 10.3390/electronics11213431 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3390/electronics11213431 (Tokushima University Institutional Repository: 118006, DOI: 10.3390/electronics11213431)
10.	Tsubasa Miyashita, Kazuyuki Matsumoto, Minoru Yoshida, Ryota Nishimura and Kenji Kita : Extraction of Lifestyle Habits Described in Blogs of Diabetics, IEEJ Transactions on Electronics, Information and Systems, Vol.142, No.10, 1144-1155, 2022. (Summary) Tackling diabetes, an increasingly common lifestyle-related illness, requires information on patients lifestyle and habits. Blogs maintained by patients afflicted with the incurable illness may be useful for analyzing how lifestyle affects health-status. In this paper, we propose an intrinsic expression extractor that extracts keywords related to lifestyle and health-status from blogs of patients diagnosed with type-2 diabetes. To counter class imbalance and add to the corpus, the proposed method tags each keyword with information based on cue-words extracted from manually tagged data. The named-entity recognition (NER) for the extracted keywords uses a bidirectional gated recurrent unit neural network (BiGRU) and was evaluated for accuracy by cross-validation. We obtained F1-score of approximately 0.76. Although the accuracy of extraction can further be improved, the novel approach has applications in analyzing and improving the lifestyle of diabetes-afflicted patients. (Keyword) disease-fighting weblogs / named entity recognition / natural language processing (Link to Publication Site) ● Publication site (DOI): 10.1541/ieejeiss.142.1144 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390293633437552768 ● Search Scopus @ Elsevier (DOI): 10.1541/ieejeiss.142.1144 (DOI: 10.1541/ieejeiss.142.1144, CiNii: 1390293633437552768)
11.	Gil P. Soriano, Yuko Yasuhara, Hirokazu Ito, Kazuyuki Matsumoto, Kyoko Osaka, Yoshihiro Kai, Rozzano Locsin, Savina Schoenhofer and Tetsuya Tanioka : Robots and Robotics in Nursing, Healthcare, Vol.10, No.8, 1571, 2022. (Keyword) artificial intelligence / robots / robotics in nursing (Tokushima University Institutional Repository) ● Metadata: 117816 (Link to Publication Site) ● Publication site (DOI): 10.3390/healthcare10081571 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3390/healthcare10081571 (Tokushima University Institutional Repository: 117816, DOI: 10.3390/healthcare10081571)
12.	Kazuyuki Matsumoto, Takumi Matsunaga, Minoru Yoshida and Kenji Kita : Emotional Similarity Word Embedding Model for Sentiment Analysis, Computacion y Sistemas, Vol.26, No.2, 875-886, 2022. (Summary) We propose a method for constructing a dictionary of emotional expressions, which is an indispensable language resource for sentiment analysis in the Japanese. Furthermore, we propose a method for constructing a language model that reproduces emotional similarity between words, which to date has yet not been considered in conventional dictionaries and language models. In the proposed method, we pretrained sentiment labels for the distributed representations of words. An intermediate feature vector was obtained from the pre-trained model. By learning an additional semantic label on this feature vector, we can construct an emotional semantic language model that embeds both emotion and semantics. To confirm the effectiveness of the proposed method, we conducted a simple experiment to retrieve similar emotional words using the constructed model. The results of this experiment showed that the proposed method can retrieve similar emotional words with higher accuracy than the conventional word-embedding model. (Keyword) Emotion recognition / emotional similarity / neural networks (Tokushima University Institutional Repository) ● Metadata: 117201 (Link to Publication Site) ● Publication site (DOI): 10.13053/CyS-26-2-4266 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.13053/CyS-26-2-4266 (Tokushima University Institutional Repository: 117201, DOI: 10.13053/CyS-26-2-4266)
13.	Kyoko Osaka, Kazuyuki Matsumoto, Akiyama Toshiya, Tanioka Ryuichi, Betriana Feni, Zhao Yueren, Kai Yoshihiro, Misao Miyagawa, Tetsuya Tanioka and Rozzano De Castro Locsin : Investigation of Methods to Create Future Multimodal Emotional Data for Robot Interactions in Patients with Schizophrenia: A Case Study, Healthcare, Vol.10, No.5, 848, 2022. (Keyword) schizophrenia / humanrobot interaction / multimodal data / multimodal emotion recognition (Tokushima University Institutional Repository) ● Metadata: 117312 (Link to Publication Site) ● Publication site (DOI): 10.3390/healthcare10050848 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3390/healthcare10050848 (Tokushima University Institutional Repository: 117312, DOI: 10.3390/healthcare10050848)
14.	Betriana Feni, Tanioka Ryuichi, Yokotani Tomoya, Kazuyuki Matsumoto, Zhao Yueren, Kyoko Osaka, Misao Miyagawa, Kai Yoshihiro, Schoenhofer Savina, Rozzano De Castro Locsin and Tetsuya Tanioka : Characteristics of interactive communication between Pepper robot, patients with schizophrenia, and healthy persons, Belitung Nursing Journal, Vol.8, No.2, 2022. (Keyword) communication / delivery of health care / robotics / schizophrenia (Tokushima University Institutional Repository) ● Metadata: 117281 (Link to Publication Site) ● Publication site (DOI): 10.33546/bnj.1998 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.33546/bnj.1998 (Tokushima University Institutional Repository: 117281, DOI: 10.33546/bnj.1998)
15.	Kazuyuki Matsumoto, Manabu Sasayama, Minoru Yoshida, Kenji Kita and Fuji Ren : Emotion Analysis and Dialogue Breakdown Detection in Dialogue of Chat Systems Based on Deep Neural Networks, Electronics, Vol.11, No.5, 2022. (Summary) In dialogues between robots or computers and humans, dialogue breakdown analysis is an important tool for achieving better chat dialogues. Conventional dialogue breakdown detection methods focus on semantic variance. Although these methods can detect dialogue breakdowns based on semantic gaps, they cannot always detect emotional breakdowns in dialogues. In chat dialogue systems, emotions are sometimes included in the utterances of the system when responding to the speaker. In this study, we detect emotions from utterances, analyze emotional changes, and use them as the dialogue breakdown feature. The proposed method estimates emotions by utterance unit and generates features by calculating the similarity of the emotions of the utterance and the emotions that have appeared in prior utterances. We employ deep neural networks using sentence distributed representation vectors as the feature. In an evaluation of experimental results, the proposed method achieved a higher dialogue breakdown detection rate when compared to the method using a sentence distributed representation vectors. (Keyword) natural language processing / dialogue breakdown / human-computer dialogue system / sentiment analysis / emotion recognition (Tokushima University Institutional Repository) ● Metadata: 116757 (Link to Publication Site) ● Publication site (DOI): 10.3390/electronics11050695 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3390/electronics11050695 (Tokushima University Institutional Repository: 116757, DOI: 10.3390/electronics11050695)
16.	Akira Fujisawa, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Emotion Estimation Method Based on Emoticon Image Features and Distributed Representations of Sentences, Applied Sciences, Vol.12, No.3, 2022. (Summary) This paper proposes an emotion recognition method for tweets containing emoticons using their emoticon image and language features. Some of the existing methods register emoticons and their facial expression categories in a dictionary and use them, while other methods recognize emoticon facial expressions based on the various elements of the emoticons. However, highly accurate emotion recognition cannot be performed unless the recognition is based on a combination of the features of sentences and emoticons. Therefore, we propose a model that recognizes emotions by extracting the shape features of emoticons from their image data and applying the feature vector input that combines the image features with features extracted from the text of the tweets. Based on evaluation experiments, the proposed method is confirmed to achieve high accuracy and shown to be more effective than methods that use text features only. (Keyword) emoticon / emotion estimation / multimodal information (Tokushima University Institutional Repository) ● Metadata: 116758 (Link to Publication Site) ● Publication site (DOI): 10.3390/app12031256 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3390/app12031256 (Tokushima University Institutional Repository: 116758, DOI: 10.3390/app12031256)
17.	Kazuyuki Matsumoto, Manabu Sasayama and Taiga Kirihara : Topic Break Detection in Interview Dialogues Using Sentence Embedding of Utterance and Speech Intention Based on Multitask Neural Networks, Sensors, Vol.22, No.2, 2022. (Summary) Currently, task-oriented dialogue systems that perform specific tasks based on dialogue are widely used. Moreover, research and development of non-task-oriented dialogue systems are also actively conducted. One of the problems with these systems is that it is difficult to switch topics naturally. In this study, we focus on interview dialogue systems. In an interview dialogue, the dialogue system can take the initiative as an interviewer. The main task of an interview dialogue system is to obtain information about the interviewee via dialogue and to assist this individual in understanding his or her personality and strengths. In order to accomplish this task, the system needs to be flexible and appropriate for detecting topic switching and topic breaks. Given that topic switching tends to be more ambiguous in interview dialogues than in task-oriented dialogues, existing topic modeling methods that determine topic breaks based only on relationships and similarities between words are likely to fail. In this study, we propose a method for detecting topic breaks in dialogue to achieve flexible topic switching in interview dialogue systems. The proposed method is based on multi-task learning neural network that uses embedded representations of sentences to understand the context of the text and utilizes the intention of an utterance as a feature. In multi-task learning, not only topic breaks but also the intention associated with the utterance and the speaker are targets of prediction. The results of our evaluation experiments show that using utterance intentions as features improves the accuracy of topic separation estimation compared to the baseline model. (Keyword) interview dialogue / topic segmentation / dialogue analysis (Tokushima University Institutional Repository) ● Metadata: 116759 (Link to Publication Site) ● Publication site (DOI): 10.3390/s22020694 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3390/s22020694 (Tokushima University Institutional Repository: 116759, DOI: 10.3390/s22020694)
18.	Kazuyuki Matsumoto, Ryota Kishima, Seiji Tsuchiya, Tomoki Hirobayashi, Minoru Yoshida and Kenji Kita : Relationship Between Personality Patterns and Harmfulness: Analysis and Prediction Based on Sentence Embedding, International Journal of Information Technology and Web Engineering, Vol.17, No.1, 1-24, 2022. (Summary) This paper hypothesize that harmful utterances need to be judged in context of whole sentences, and extract features of harmful expressions using a general-purpose language model. Based on the extracted features, we propose a method to predict the presence or absence of harmful categories. In addition, the authors believe that it is possible to analyze users who incite others by combining this method with research on analyzing the personality of the speaker from statements on social networking sites. The results confirmed that the proposed method can judge the possibility of harmful comments with higher accuracy than simple dictionary-based models or models using a distributed representations of words. The relationship between personality patterns and harmful expressions was also confirmed by an analysis based on a harmful judgment model. (Keyword) Deep Neural Networks / Harmful Expression / Internet Flaming Detection / MBTI / Personality (Tokushima University Institutional Repository) ● Metadata: 116756 (Link to Publication Site) ● Publication site (DOI): 10.4018/IJITWE.298654 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1360298336571568640 ● Search Scopus @ Elsevier (DOI): 10.4018/IJITWE.298654 (Tokushima University Institutional Repository: 116756, DOI: 10.4018/IJITWE.298654, CiNii: 1360298336571568640)
19.	Kazuyuki Matsumoto, Seiji Tsuchiya, Minoru Yoshida and Kenji Kita : Construction and Expansion of Dictionary of Idiomatic Emotional Expressions and Idiomatic Emotional Expression Corpus, International Journal of Computer & Software Engineering, Vol.6, No.2, 2021. (Summary) Objective: In the study of sentiment estimation from language, methods focusing on words, phrases, sentence patterns, and sentence-final expressions have been proposed. However, it is difficult to deal with a wide variety of emotional expressions by only assigning emotions to words and phrases. In particular, it is difficult to analyze metaphorical expressions and idiomatic expressions on a word-by-word basis, and it is impossible to register all expressions in a dictionary because new expressions can be created byflexibly replacing words. However, it is difficult to determine the constraints on the words to be replaced, and not all expressions can be registered in the dictionary as sentence patterns.Methods: In this paper, we construct a dictionary of idiomatic sentiment expressions, which contains idioms expressing emotions. In this paper, we construct a pseudo-emotional corpus by collecting utterances containing emotional idioms from social media and automatically assigning emotions expressed by the idioms.Results: This corpus includes expressions other than idioms, and can be an effective resource for estimating emotions in sentences that do not contain idioms. In this study, we create an emotion estimation model for utterances based on the constructed corpus, and conduct evaluation experiments to explore the problems of the idiomatic emotion corpus. In addition, using the constructed sentimentcorpus, we investigate how to expand the dictionary of sentiment expressions in idiomatic phrases by using deep learning methods.Conclusion: Using the corpus of idiomatic sentiments constructed by the proposed method as training data, models with and without idioms were constructed by machine learning models. The results show that the F-values of all emotions with idioms exceed 0.8. On the other hand, when idioms were not included, the F-values tended to decrease overall. However, the F-values of emotions such as "shame"and "excitement" were around 0.7, indicating that the characteristics of emotional expressions other than idioms were expressed. (Keyword) emotion recognition / idiomatic expression / idiomatic emotional expression / corpus (Tokushima University Institutional Repository) ● Metadata: 116606 (Link to Publication Site) ● Publication site (DOI): 10.15344/2456-4451/2021/174 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.15344/2456-4451/2021/174 (Tokushima University Institutional Repository: 116606, DOI: 10.15344/2456-4451/2021/174)
20.	Reishi Amitani, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning, Applied Sciences, Vol.11, No.22, 2021. (Summary) This study investigates social media trends and proposes a buzz tweet classification method to explore the factors causing the buzz phenomenon on Twitter. It is difficult to identify the causes of the buzz phenomenon based solely on texts posted on Twitter. It is expected that by limiting the tweets to those with attached images and using the characteristics of the images and the relationships between the text and images, a more detailed analysis than that of with text-only tweets can be conducted. Therefore, an analysis method was devised based on a multi-task neural network that uses both the features extracted from the image and text as input and the buzz class (buzz/non-buzz) and the number of likes (favorites) and retweets (RTs) as output. The predictions made using a single feature of the text and image were compared with the predictions using a combination of multiple features. The differences between buzz and non-buzz features were analyzed based on the cosine similarity between the text and the image. The buzz class was correctly identified with a correctness rate of approximately 80% for all combinations of image and text features, with the combination of BERT and VGG16 providing the highest correctness rate (Keyword) multi-task learning / buzz classification / social media / trend analysis (Tokushima University Institutional Repository) ● Metadata: 116681 (Link to Publication Site) ● Publication site (DOI): 10.3390/app112210567 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3390/app112210567 (Tokushima University Institutional Repository: 116681, DOI: 10.3390/app112210567)
21.	Tetsuya Tanioka, Tomoya Yokotani, Tanioka Ryuichi, Betriana Feni, Kazuyuki Matsumoto, Rozzano De Castro Locsin, Zhao Yueren, Kyoko Osaka, Misao Miyagawa and Schoenhofer Savina : Development Issues of Healthcare Robots: Compassionate Communication for Older Adults with Dementia, International Journal of Environmental Research and Public Health, Vol.18, No.9, 4538, 2021. (Keyword) artificial intelligence / geriatric care / healthcare robots / intelligent humanoid robots / natural language processing / older persons / robot compassion (Tokushima University Institutional Repository) ● Metadata: 116338 (Link to Publication Site) ● Publication site (DOI): 10.3390/ijerph18094538 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 33923353 ● Search Scopus @ Elsevier (PMID): 33923353 ● Search Scopus @ Elsevier (DOI): 10.3390/ijerph18094538 (Tokushima University Institutional Repository: 116338, DOI: 10.3390/ijerph18094538, PubMed: 33923353)
22.	Yokotani Tomoya, Tanioka Ryuichi, Kawai Chihiro, Betriana Feni, Hirokazu Ito, Yuko Yasuhara, Tetsuya Tanioka, Rozzano De Castro Locsin, Kazuyuki Matsumoto and Fuji Ren : Human Psychological Burden and Thinking Process while Operating a Humanoid Robot Conversation App Program, International Journal of Advanced Intelligence (IJAI), Vol.12, No.1, 23-35, 2021. (Keyword) Older adults / communication robot / Pepper robot / natural language processing / artificial intelligence / human thinking process / psychological burden
23.	Tanioka Ryuichi, Kazuyuki Matsumoto, Sugawara Kenichi, Takase Kensaku, Kai Yoshihiro, Masahito Tomotake, Tetsuya Tanioka and Rozzano De Castro Locsin : Practical issues in Developing Natural Language Processing Applications for Healthcare Robot Motivation of Older Persons Physical Rehabilitation, International Journal of Advanced Intelligence (IJAI), Vol.12, No.1, 83-90, 2021. (Keyword) natural language processing / communication and rehabilitation humanoid robots / data applications / clinical evaluation / interdisciplinary research / older person
24.	Hirokazu Ito, Kazuyuki Matsumoto, XIN KANG, Tetsuya Tanioka, Yuko Yasuhara, Rozzano De Castro Locsin and Fuji Ren : The AI Development Through Transforming Tacit Knowledge to Explicit Knowledge of Nurses' Dialogue for Patients with Dementia, International Journal of Advanced Intelligence (IJAI), Vol.12, No.1, 11-21, 2021. (Keyword) natural language processing / artificial intelligence / dementia care / PsyNACS / explicit and tacit knowledge / nurses' dialogue / humanoid robot (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1010576118604664832 (CiNii: 1010576118604664832)
25.	Yuki Obayashi, Feni Betriana, Tetsuya Tanioka, Tomoya Yokotani, Ryuichi Tanioka, Chihiro Kawai, Hirokazu Ito, Yuko Yasuhara, Rozzano De Castro Locsin, Kyoko Osaka, Kazuyuki Matsumoto, Fuji Ren and Yoshihiro Kai : Developmental Issues of Communication for Robot Applications in Older People Care: An Integrative Review, International Journal of Advanced Intelligence (IJAI), Vol.12, No.1, 53-67, 2021. (Keyword) Communication robots / applications / performance of humanoid robots / developmental issues
26.	Minoru Yoshida, Kojima Takumi, Kazuyuki Matsumoto and Kenji Kita : Toward Analyzing Relations between Sleep Time and Social Networking Service Texts: Prediction of the Tweet Time Span Using the Last Tweet of the Day, International Journal of Advanced Intelligence (IJAI), Vol.12, No.1, 1-9, 2021. (Summary) Sleeping habits are one of the major issues in today's healthcare. In this paper, we consider the problem of analyzing sleeping habits of people using social networking service (SNS) texts. As the first step toward predicting user's sleeping time using SNS texts, we assume that the time span between the user's last post in one day and the first post the next day can be used as a pseudo-indicator for the user's sleeping time if the user posts the text sufficiently frequently. We call such tweet time spans ``pseudo-sleeping time'' if the first tweet of the next day include ``Good morning'' or similar words. We try to predict such pseudo-sleeping time using the text (tweet) of the preceding tweet (i.e., the last tweet of the day). Preliminary experiments show that the tweet text contains some useful information to predict the user's pseudo-sleeping time. (Keyword) sleeping time / SNS / text mining (Tokushima University Institutional Repository) ● Metadata: 116122 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1570291227698842880 (Tokushima University Institutional Repository: 116122, CiNii: 1570291227698842880)
27.	Betriana Feni, Kyoko Osaka, Kazuyuki Matsumoto, Tetsuya Tanioka and Rozzano De Castro Locsin : Relating Moris Uncanny Valley in generating conversations with artificial affective communication and natural language processing, Nursing Philosophy, Vol.22, No.2, e12322, 2020. (Link to Publication Site) ● Publication site (DOI): 10.1111/nup.12322 (Link to Search Site for Scientific Articles) ● PubMed @ National Institutes of Health, US National Library of Medicine (PMID): 32803785 ● Summary page in Scopus @ Elsevier: 2-s2.0-85089456003 (DOI: 10.1111/nup.12322, PubMed: 32803785, Elsevier: Scopus)
28.	Asaki Kataoka, Manabu Sasayama and Kazuyuki Matsumoto : Data Augmentation for Lyrics Emotion Estimation, ASM Science Journal, Vol.13, No.SI3, 11-16, 2020. (Summary) Lyrics emotion estimation can allow us to realise song retrieval systems and song recommendationsystems which are based on not only text retrieval nor melody matching but also emotions in lyricsor transitions of emotions within lyrics of a whole song. This requires lyrics emotion corpora ofphrase. However, it is difficult to build large scale lyrics emotion corpora because emotions arelabelled manually. In this paper, we propose a method to augment lyrics emotion corpora. As a result,we augmented a corpus consisting of 366 phrases into a larger corpus consisting of 2145 phrases. Wealso evaluate the proposed method using 2 convolutional neural networks trained on original corpusand augmented corpus respectively. We define the target emotion classes as Joy, Love, Anger, Sorrowand Anxiety. Mean accuracy of the model trained on the augmented corpus was 75.9% whilst themodel trained on the original corpus performed 70.7%. (Keyword) lyrics emotion estimation / text data augmentation / word2vec / convolutional neural network (Tokushima University Institutional Repository) ● Metadata: 116446 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85091556762 (Tokushima University Institutional Repository: 116446, Elsevier: Scopus)
29.	Yuko Sakuta, Junichi Tsunobe, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Estimating Psychological Impressions from Color Images Using Earth Mover's Distance, Transactions of Japan Society of Kansei Engineering, Vol.19, No.1, 97-104, 2020. (Summary) In this paper, we describe a method for estimating psychological impressions (e.g., Pretty, Elegant, Romantic, Cool, Formal, etc) from color images. Essentially, the Earth Movers Distance (EMD) is employed to map a given image onto the color image scale, a two-dimensional emotion space comprised of a WARM-COOL axis and a SOFT-HARD axis, on which many color combinations and Kansei words are located. To validate our proposed method, we collected real images from the Web and compared our EMD-based results with human evaluation results. The comparison demonstrated that EMD-based Kansei mapping is a promising method for extracting psychological impressions from color images. (Keyword) Earth Mover's Distance / EMD / Psychological impressions / image processing / Color image scale / emotion / Kansei mapping / Color images (Link to Publication Site) ● Publication site (DOI): 10.5057/jjske.TJSKE-D-19-00038 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390002184878233472 ● Search Scopus @ Elsevier (DOI): 10.5057/jjske.TJSKE-D-19-00038 (DOI: 10.5057/jjske.TJSKE-D-19-00038, CiNii: 1390002184878233472)
30.	Kazuyuki Matsumoto, Seiji Tsuchiya, Takumi Kojima, Hiroya Kondo, Minoru Yoshida and Kenji Kita : Classification of Smartphone Application Reviews Using Small Corpus Based on Bidirectional LSTM Transformer, International Journal of Machine Learning and Computing, Vol.10, No.1, 148-157, 2020. (Summary) This paper provides the classification of the review texts on a smartphone application posted on social media. We propose a high performance binary classification method (positive/negative) of review texts, which uses the bidirectional long short-term memory (biLSTM) self-attentional Transformer and is based on the distributed representations created by unsupervised learning of a manually labelled small review corpus, dictionary, and an unlabeled large review corpus. The proposed method obtained higher accuracy as compared to the existing methods, such as StarSpace or the Bidirectional Encoder Representations from Transformer (BERT). (Keyword) attention mechanism / review classification / small corpus / transformer (Tokushima University Institutional Repository) ● Metadata: 114311 (Link to Publication Site) ● Publication site (DOI): 10.18178/ijmlc.2020.10.1.912 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.18178/ijmlc.2020.10.1.912 (Tokushima University Institutional Repository: 114311, DOI: 10.18178/ijmlc.2020.10.1.912)
31.	Hidetoshi Nakao, 今岡真和, 岡健司, 森藤武, 橋本雅至, Kazuyuki Matsumoto and Kenji Kita : Factor Analysis of Individual Factors Associated with Low Arch Height Ratio of the Foot by SVM-RFE, Transactions of the Japanese Society for Medical and Biological Engineering, Vol.57, No.6, 190-197, 2019. (Keyword) SVM-RFE (Link to Publication Site) ● Publication site (DOI): 10.11239/jsmbe.57.190 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390002184878527360 ● Summary page in Scopus @ Elsevier: 2-s2.0-85081002741 (DOI: 10.11239/jsmbe.57.190, CiNii: 1390002184878527360, Elsevier: Scopus)
32.	中尾英俊, 今岡真和, 岡健司, 肥田光正, 森藤武, 橋本雅至, Kazuyuki Matsumoto and Kenji Kita : 健常者の足背高測定による足部アーチ評価とアーチ高に影響を及ぼす因子, Journal of the Society of Biomechanisms, Vol.43, No.4, 256-262, 2019. (Link to Publication Site) ● Publication site (DOI): 10.3951/sobim.43.4_256 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001277380551168 ● Search Scopus @ Elsevier (DOI): 10.3951/sobim.43.4_256 (DOI: 10.3951/sobim.43.4_256, CiNii: 1390001277380551168)
33.	Koji Bando, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Twitter User's Hobby Estimation Based on Sequential Statements Using Deep Neural Networks, International Journal of Machine Learning and Computing, Vol.9, No.2, 108-114, 2019. (Summary) With more and more frequency, users communicate with each other on social media. Many users start on Twitter or Facebook to find friends who have the same hobby. Our study proposes a method to estimate the users interests (hobby) based on tweets on Twitter. One tweet does not, in and of itself, contain a lot of information, and some tweets are not related to the users hobby. Therefore, we propose a reliable hobby estimation method by extracting features from multiple, sequential tweets. The proposed method uses Recurrent Neural Networks (RNN) which can accommodate time-series information. We also used a Convolutional Neural Networks (CNN) which can treat contextual information. We used an averaged vector of word distributed representation as a feature. Using the proposed method based on Long Short-Term Memory Recurrent Neural Networks (LSTM-RNN), we obtained a 23.72% improvement as compared with a baseline method using a Random Forest (RF) regression as a machine learning algorithm. (Keyword) Twitter / hobby estimation / neural network / random forests / support vector machine (Tokushima University Institutional Repository) ● Metadata: 113915 (Link to Publication Site) ● Publication site (DOI): 10.18178/ijmlc.2019.9.1.773 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85064990114 (Tokushima University Institutional Repository: 113915, DOI: 10.18178/ijmlc.2019.9.1.773, Elsevier: Scopus)
34.	Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Emotion Recognition for Japanese Short Sentences Including Slangs Based on Bag of Concepts Feature Trained by Large Web Text, Current Analysis on Instrumentation and Control, Vol.2019, No.2, 9-18, 2019. (Summary) The growth of Internet communication sites such as weblogs and social networking sites brought younger people especially in teens and in their 20s to create new words and to use them very often. We prepared an emotion corpus by collecting weblog article texts including new words, analyzed the corpus statistically, and proposed a method to estimate emotions of the texts. Most slang words such as Youth Slang are too ambiguous in sense classification to be registered into the existing dictionaries such as thesaurus. To cope with these words, we created a large scale of Twitter corpus and calculated sense similarities between words. We proposed to convert unknown word to semantic class id so that we might be able to process the words that were not included in the learning data. For calculation similarities between words and converting the word into word cluster id, we used the word embedding algorithms such as word2vec, or GloVe. We defined this method as a method using Bag of Concepts as feature. As a result of the evaluation experiment using several classifiers, the proposed method was proved its robustness for unknown expressions. (Keyword) Youth Slang / Unknown Words / Bag of Concepts / Word Embedding / Unsupervised Clustering (Tokushima University Institutional Repository) ● Metadata: 113245 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1571980077723683712 (Tokushima University Institutional Repository: 113245, CiNii: 1571980077723683712)
35.	Kazuyuki Matsumoto, Fuji Ren, Masaya Matsuoka, Minoru Yoshida and Kenji Kita : Slang Feature Extraction by Analyzing Topic Change on Social Media, CAAI Transactions on Intelligence Technology, 2019. (Summary) Recently, we often see words such as youth slang, neologism and Internet slang on social networking sites (SNSs) that are not registered on dictionaries. Because the documents posted to SNSs include a lot of fresh information, they are thought to be useful for collecting information. It is important to analyze these words (hereinafter referred to as slang) and capture their features for the improvement of the accuracy of automatic information collection. This paper aims to analyze what features can be observed in slang by focusing on the topic. We construct topic models from document groups including target slang on Twitter by Latent Dirichlet Allocation (LDA). With the models, we chronologically analyze change of topics during a certain period of time to find out the difference in the features between slang and general words. Then, we propose a slang classification method based on the change of features. (Keyword) Slang / Topic analysis / Latent Dirichlet Allocation / Social media, / Tweet representation (Tokushima University Institutional Repository) ● Metadata: 113246 (Link to Publication Site) ● Publication site (DOI): 10.1049/trit.2018.1060 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1049/trit.2018.1060 (Tokushima University Institutional Repository: 113246, DOI: 10.1049/trit.2018.1060)
36.	Kazuyuki Matsumoto, Akira Fujisawa, Minoru Yoshida and Kenji Kita : ASCII Art Classification based on Deep Neural Networks Using Image Feature of Characters, Journal of Software, Vol.13, No.10, 559-572, 2018. (Summary) In recent years, a lot of non-verbal expressions have been used on social media. Ascii art (AA) is an expression using characters with visual technique. In this paper, we set up an experiment to classify AA pictures by using character features and image features. We try to clarify which feature is more effective for a method to classify AA pictures. We proposed four methods: 1) a method based on character frequency, 2) a method based on character importance value and 3) a method based on image features and 4) a method based on image features of characters. We trained neural networks by using these four features. In the experimental result, the best classification accuracy was obtained in the feed forward neural networks that used image features of characters. (Keyword) ASCII Art / classification / deep neural network / character feature (Tokushima University Institutional Repository) ● Metadata: 113248 (Link to Publication Site) ● Publication site (DOI): 10.17706/jsw.13.10.559-572 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.17706/jsw.13.10.559-572 (Tokushima University Institutional Repository: 113248, DOI: 10.17706/jsw.13.10.559-572)
37.	Mei Chen, Qingmei Xiao, Kazuyuki Matsumoto, Minoru Yoshida, Kenji Kita and Xin Luo : Similarity Analysis and Repeating Pattern Detection in Fingerprint Features, Journal of Computational Methods in Sciences and Engineering, Vol.18, No.3, 553-562, 2018. (Keyword) audio fingerprint / similarity analysis / repeating pattern detection / music retrieval (Link to Publication Site) ● Publication site (DOI): 10.3233/JCM-180781 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85059945684 (DOI: 10.3233/JCM-180781, Elsevier: Scopus)
38.	Kazuyuki Matsumoto, Kyosuke Akita, Fuji Ren, Minoru Yoshida and Kenji Kita : Intimacy Estimation of the Characters in Drama Scenario, Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, Vol.30, No.3, 591-604, 2018. (Summary) Recently, a portable digital device equipped with voice guidance has been widely used, increasing the demand for the usabilityconscious dialogue system. One of the problems with the existing dialogue system is its immature application to non-task-orienteddialogue. Non-task-oriented dialogue requires some scheme that enables smooth and flexible conversations with a user. For example,it would be possible to provide topics related to a person who is familiar with a user or avoid providing topics related to a personwho is not a good friend with the user, by considering relationship with others in real life of the user. In this paper, we focus on thedialogue made by the two characters in a drama scenario, and tried to express their relationship with a scale of intimacy degree.There will be such various elements related to the intimacy degree as the frequency of response to the utterance and the attitude ofa speaker during the dialogue. We focus on the emotional state of the speaker during the utterance and try to realize an intimacyestimation with high accuracy. As the evaluation result, we achieved high accuracy in intimacy estimation than the existing methodbased on speech role. (Keyword) intimacy degree / scenario dialogue / emotional state (Tokushima University Institutional Repository) ● Metadata: 113247 (Link to Publication Site) ● Publication site (DOI): 10.3156/jsoft.30.3_591 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390564237991620096 ● Search Scopus @ Elsevier (DOI): 10.3156/jsoft.30.3_591 (Tokushima University Institutional Repository: 113247, DOI: 10.3156/jsoft.30.3_591, CiNii: 1390564237991620096)
39.	Fujino Naoya, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Emotion Estimation Adapted to Gender of User Based on Deep Neural Networks, International Journal of Advanced Intelligence (IJAI), Vol.10, No.1, 121-133, 2018. (Summary) In this study, we focus on Twitter as a representative SNS and target emotion estima-tion from tweets posted on Twitter by male and female users. Specically, we constructgender-based emotion estimation models assuming that there are different word usage ten-dencies between genders. By analyzing gender-specic differences in the use of emotion-related slang and emoji, we propose a method to improve emotion estimation based onneural networks using a different distributed representation model for each gender. Ourevaluation experiments show that training with Deep Convolutional Neural Networks us-ing word's distributed representation as the feature produced higher estimation accuracythan training with Feed Forward Neural Networks. (Keyword) emotion estimation / user's gender, / deep neural networks (Tokushima University Institutional Repository) ● Metadata: 113250 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1050302172853082368 (Tokushima University Institutional Repository: 113250, CiNii: 1050302172853082368)
40.	Hirokimi Fukuda, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Index Generation of BGM Video Based on Distinctive Comments, International Journal of Advanced Intelligence (IJAI), Vol.10, No.1, 113-119, 2018. (Summary) The purpose of this study is to generate indexes for background music (BGM) based ondistinctive comments annotated to BGM videos posted on Nico Nico Videos. Our proposedmethod detects the end/start positions of the BGM considering the increase/decrease inthe number of comments, comments distinctive to the songs, and exclamatory expressionsannotated to the videos. The evaluation result indicated that the proposed method couldgenerate correct indexes within a 10 s error for BGM videos less than 40 min long andwith annotations of over 30,000 comments. (Keyword) Nico Nico Video / Index generation / BGM video for work (Tokushima University Institutional Repository) ● Metadata: 113249 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1050020697876372224 (Tokushima University Institutional Repository: 113249, CiNii: 1050020697876372224)
41.	Kazuyuki Matsumoto, Seiji Tsuchiya, Miyake Takeshi, Minoru Yoshida and Kenji Kita : Flame Prediction Based on Harmful Expression Judgement Using Distributed Representation, International Journal of Technology and Engineering Studies, Vol.4, No.1, 7-15, 2018. (Summary) In recent years, flaming-that is, hostile or insulting interaction-on social media has been a problem. To avoid or minimize flaming, enabling the system to automatically check messages before posting to determine whether they include expressions that are likely to trigger flaming can be helpful. We target two types of harmful expressions: insulting expressions and expressions that are likely to cause a quarrel. We first constructed an original harmful expressions dictionary. To minimize the cost of collecting the expressions, we built our dictionary semi-automatically by using word distributed representations. The method used distributed representations of harmful expressions and general expressions as features, and constructed a classifier of harmful/general expressions based on these features. Anevaluation experiment found that the proposed method was able to extract harmful expressions with an accuracy of approximately 70%. The proposed method was also able to extract unknown expressions; however, it tended to wrongly extract non-harmful expressions. The method is able to determine unknown harmful expressions not included in the basic dictionary and can identify semantic relationships among harmful expressions. Although the method cannotpresently be applied directly to multi-word expressions, it should be possible to add such a capability by introducing time-series learning. (Keyword) flame prediction / harmful expression / distributed representation / support vector machine (Tokushima University Institutional Repository) ● Metadata: 113251 (Link to Publication Site) ● Publication site (DOI): 10.20469/ijtes.4.10002-1 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1572261552700328448 ● Search Scopus @ Elsevier (DOI): 10.20469/ijtes.4.10002-1 (Tokushima University Institutional Repository: 113251, DOI: 10.20469/ijtes.4.10002-1, CiNii: 1572261552700328448)
42.	Fuji Ren and Kazuyuki Matsumoto : Emotion Analysis on Social Big Data, ZTE Communications, Vol.15, No.S2, 30-37, 2017. (Summary) In this paper, we describe a method of emotion analysis on social big data. Social big data means text data that is emerging on In ternet social networking services.We collect multilingual web corpora and annotated emotion tags to these corpora for the purpose of emotion analysis. Because these data are constructed by manual annotation, their quality is high but their quantity is low. If we create an emotion analysis model based on this corpus with high quality and use the model for the analysis of social big data, we might be able to statistically analyze emotional sensesand behavior of the people in Internet communications, which we could not know before. In this paper, we create an emotion analysis model that integrate the high quality emotion corpus and the automatic constructed corpus that we created in our past studies, and then analyze a large scale corpus consisting of Twitter tweets based on the model. As the result of time series analysis on the large scale corpus and the result of model evaluation, we show the effective ness of our proposed method. (Link to Publication Site) ● Publication site (DOI): 10.3969/j.issn.1673-5188.2017.S2.005 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3969/j.issn.1673-5188.2017.S2.005 (DOI: 10.3969/j.issn.1673-5188.2017.S2.005)
43.	Kazuyuki Matsumoto, Fuji Ren, Minoru Yoshida and Kenji Kita : Review Score Estimation Based on Transfer Learning of Different Media Review Data, International Journal of Advanced Intelligence (IJAI), Vol.9, No.4, 541-555, 2017. (Summary) We propose a model to classify reviews based on review data from different media sources. Recently, research has been actively conducted on transfer learning between different domains with various kinds of big data as the target. The fact that evaluation expressions often vary in different domains presents a barrier to reputation analysis. Users commonly use various linguistic expressions to refer to creative works, depending on the specific media form.For example, the terms or expressions used in anime to describe creative works within that medium are different from the expressions used in comics, or games or movies. These differences can be considered as features of each individual medium. We should expect, then, that there would be differences in evaluation expressions among the various media, as well. We analyze the effects of such differences on classification accuracy by conducting transfer learning between review data from different media and demonstrate compatibility between the original (pre-transfer) and target (post-transfer) media by constructing a review classification model. As a result of our evaluation experiments, we are able to more accurately estimate review scores without using SO-Scores for training review fragments based on Long Short-Term Memory (LSTM) rather than using a method based on SO-Scores. (Keyword) review classification / transfer learning / Long Short-Term Memory / different media (Tokushima University Institutional Repository) ● Metadata: 113252 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1570572702840072192 (Tokushima University Institutional Repository: 113252, CiNii: 1570572702840072192)
44.	Kazuyuki Matsumoto, Akira Fujisawa, Minoru Yoshida and Kenji Kita : Emotion Recognition of Emoticon Based on Character Embedding, Journal of Software, Vol.12, No.11, 849-857, 2017. (Summary) This paper proposes a method for emotion estimation of emoticon based on distributed representations of character meanings of the emoticon. The existing studies on emoticon have tried to extract emoticons from texts, estimate emotions of emoticons by breaking them up into constituent parts and by using their combination as feature. Based on the recently developed technique for word embedding, we try to realize versatile emotion estimation from emoticons by training the meanings of the characters constituting the emoticons and by using them as feature unit of emoticon based on the recent developed word embedding technique. The cross validation test was conducted for the emoticons emotion estimation model that was constructed based on the deep convolutional neural networks using distributed representations of characters as feature, and its result showed that our proposed method can estimate emotion of unknown emoticons with higher F1-score than the method based on the character n-gram appearance frequency vector as feature. (Keyword) emoticon / emotion recognition / character embedding / convolutional neural networks (Tokushima University Institutional Repository) ● Metadata: 113914 (Link to Publication Site) ● Publication site (DOI): 10.17706/jsw.12.11.849-857 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.17706/jsw.12.11.849-857 (Tokushima University Institutional Repository: 113914, DOI: 10.17706/jsw.12.11.849-857)
45.	Yunong Wu, Kenji Kita, Kazuyuki Matsumoto, XIN KANG and Fuji Ren : Constructing A Short Text Conversation system Based on the Relations Between Posts and Comments, International Journal of Advanced Intelligence (IJAI), Vol.9, No.3, 369-380, 2017.
46.	Zhang Guodong, Jiang Peiling, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : An Improvement of Pedestrian Detection Method with Multiple Resolutions, Journal of Computer and Communications, Vol.5, No.9, 102-116, 2017. (Summary) In object detection, detecting an object with 100 pixels is substantially different from detecting an object with 10 pixels. Many object detection algorithms assume that the pedestrian scale is fixed during detection, such as the DPM detector. However, detectors often give rise to different detection effects under the circumstance of different scales. If a detector is used to perform pedestrian detection in different scales, the accuracy of pedestrian detection could be improved. A multi-resolution DPM pedestrian detection algorithm is proposedin this paper. During the stage of model training, a resolution factor is added to a set of hidden variables of a latent SVM model. Then, in the stage of detection, a standard DPM model is used for the high resolution objects and a rigid template is adopted in case of the low resolution objects. In our experiments, we find that in case of low resolution objects the detection accuracy of a standard DPM model is lower than that of a rigid template. In Caltech, the omission ratio of a multi-resolution DPM detector is 52% with 1 false positive per image (1FPPI); and the omission ratio rises to 59% (1FPPI) as far as a standard DPM detector is concerned. In the large-scale sample set of Caltech, the omission ratios given by the multi-resolution and the standard DPM detectors are 18% (1FPPI) and 26% (1FPPI), respectively. (Keyword) Deformable Part Model / Pedestrian Detection / Multi-Resolution / Latent SVM (Tokushima University Institutional Repository) ● Metadata: 113913 (Link to Publication Site) ● Publication site (DOI): 10.4236/jcc.2017.59007 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.4236/jcc.2017.59007 (Tokushima University Institutional Repository: 113913, DOI: 10.4236/jcc.2017.59007)
47.	Kazuyuki Matsumoto, Satoshi Tanaka, Minoru Yoshida, Kenji Kita and Fuji Ren : Ego-state Estimation from Short Texts Based on Sentence Distributed Representation, International Journal of Advanced Intelligence (IJAI), Vol.9, No.2, 145-161, 2017. (Summary) Human personality multilaterally consists of complex elements. Egogram is a method to classify personalities into the patterns according to the combinations of the five levels of ego-states. With recent development of Social Networking Service (SNS), more researches have been trying to judge personality from the statements on SNS. However, there are several problems in the personality judgment based on the superficial information of the statements. Concretely, personality is not always reflected on every statement and the tendency of the statements with influence of personality changes with the times. It is also important to collect sufficient amount of statement data including the results of personality judgment. In this paper, to realize automatic egogram judgement, we focused on the short texts on the SNS, especially microblogs, and represented the comments on Twitter by distributed representation (sentence vector) in the pre-training. Then, we tried to create a model to estimate ego-state level of each user by using a deep neural network. The experimental result showed that our proposed method estimated ego-state with higher accuracy than the baseline method based on bag of words. To investigate the change of personality according to time, we also analyzed how the match rates of the estimation results changed before/after the egogram judgment. Moreover, we confirmed that the personality pattern classification was improved by adding a feature expressing the formal degree of the sentence. (Keyword) Egogram / Personality Estimation / Twitter / Social Networking Service / Distributed Representation (Tokushima University Institutional Repository) ● Metadata: 113455 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1050583647829752704 (Tokushima University Institutional Repository: 113455, CiNii: 1050583647829752704)
48.	Kazuyuki Matsumoto, Seiji Tsuchiya, Imono Misako, Minoru Yoshida and Kenji Kita : Conversion of Japanese Slang into Standard Japanese Considering Sensibility, Transactions of the Japanese Society for Artificial Intelligence, Vol.32, No.1, WII-A_1-12, 2017. (Summary) With the recent spread of communication using social media, exchanging opinions each other on web has become more common irrespective of age and sex. On the other hand, a problem called as ``Internet flaming'' often occurs along with the increase of social network service users. One of the reasons might be that the users do not recognize meanings/intentions/emotions expressed by other users' words. In this study, we focused on slangs (Internet slangs) that are often used on SNS but are not registered in dictionaries, then tried to convert them into standard words. We also intended to output more appropriate candidates by considering not only semantic similarity but also affective similarity. The proposed method conducts filtering and re-ranking over the semantically similar candidates obtained based on distributed representations to detect the inappropriate candidates as standard word by focusing on two points: (1) features of slang/standard word and (2) affective similarity between the inputted word and the candidate words. In the evaluation experiment, the proposed method obtained a higher MRR than the baseline method. (Keyword) Internet slang / standard word / sensibility / concept of word (Tokushima University Institutional Repository) ● Metadata: 113254 (Link to Publication Site) ● Publication site (DOI): 10.1527/tjsai.WII-A (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1527/tjsai.WII-A (Tokushima University Institutional Repository: 113254, DOI: 10.1527/tjsai.WII-A)
49.	Zhang Guodong, Jiang Peilin, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Reidentification of Persons Using Clothing Features in Real-Life Video, Applied Computational Intelligence and Soft Computing, Vol.2017, 1-9, 2017. (Summary) Person reidentification, which aims to track people across nonoverlapping cameras, is a fundamental task in automated video processing. Moving people often appear differently when viewed from different nonoverlapping cameras because of differences in illumination, pose, and camera properties. The color histogram is a global feature of an object that can be used for identification. This histogram describes the distribution of all colors on the object. However, the use of color histograms has two disadvantages. First, colors change differently under different lighting and at different angles. Second, traditional color histograms lack spatial information. We used a perception-based color space to solve the illumination problem of traditional histograms. We also used the spatial pyramid matching (SPM) model to improve the image spatial information in color histograms. Finally, we used the Gaussian mixture model (GMM) to show features for person reidentification, because the main color feature of GMM is more adaptable for scene changes, and improve the stability of the retrieved results for different color spaces in various scenes. Through a series of experiments, we found the relationships of different features that impact person reidentification. (Keyword) Clothing Feature / Gaussian mixture mode / spatial pyramid matching / person reidentification / perception-based color space (Tokushima University Institutional Repository) ● Metadata: 113911 (Link to Publication Site) ● Publication site (DOI): 10.1155/2017/5834846 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1155/2017/5834846 (Tokushima University Institutional Repository: 113911, DOI: 10.1155/2017/5834846)
50.	Kazuyuki Matsumoto, Minoru Yoshida, Seiji Tsuchiya, Kenji Kita and Fuji Ren : Slang Analysis Based on Variant Information Extraction Focusing on the Time Series Topics, International Journal of Advanced Intelligence (IJAI), Vol.8, No.1, 84-98, 2016. (Summary) Recently, with increase of the number of users of Social Networking Sites, online communications have been more and more actively made, raising the possibility to use the big data on SNS for analyzing the diversity of language. Japanese language uses varieties of character types and such character types are combined and used for creating words and phrases. Therefore, it is difficult to morphologically analyze such words and phrases even though morphological analysis is a basic processing in natural language processing. Such words and phrases that are not registered in morphological analysis dictionaries are usually not defined strictly and semantic interpret for them seems to vary depending on deindividual. In this study, we chronologically analyze the topics related to slang on Twitter. In this paper, as a validation experiment, we conducted a topic analysis experiment chronogically by using the sequential tweet data, and discussed the difference of topic change according to the slang types. (Keyword) Slang / Topic analysis / Time-series analkysis (Tokushima University Institutional Repository) ● Metadata: 113910 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1571698602718115840 (Tokushima University Institutional Repository: 113910, CiNii: 1571698602718115840)
51.	Akira Fujisawa, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : An Illustration Image Classification Focusing on Infrequent Colors, International Journal of Advanced Intelligence (IJAI), Vol.8, No.1, 72-83, 2016. (Summary) Illustration images used in comics or animation works have the style which is emotional feature. The style is represented by the various elements of the illustration image. There are a few researches which treat the style of illustration image is fewer. In this paper, we tried to model the style by using an image feature, and to classify an illustration image by the style. We thought that the color which appeared infrequently represent the style of illustration image. Therefore, we proposed the method for creating the color histogram which emphasize colors appearing infrequently. We called that as Infrequency histogram; IF-hist. To test effectiveness of the histogram, we experimented to classify the two styles which are For boys and For girls based on the IF-hist. As the result of the experiment, the case using the IF-hist, the precision of classification result by the style For girls is 93%. It is 50% higher than the case using existing normal color histogram. Moreover the precision of the other classification results have improved. (Keyword) Image Classification / Illustration Image / Color Histogram / Style / IF-hist (Tokushima University Institutional Repository) ● Metadata: 113909 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1050583647829579648 (Tokushima University Institutional Repository: 113909, CiNii: 1050583647829579648)
52.	Sasayama Manabu and Kazuyuki Matsumoto : Retrieving Vaguely Remembered Lyrics Using N-Gram Edit Distance, International Journal of Advanced Intelligence (IJAI), Vol.8, No.1, 1-11, 2016. (Keyword) Vaguely remembered lyrics / Spelling variants / Kana / n-gram Levenshtein distance (Tokushima University Institutional Repository) ● Metadata: 113908 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1050865122806290304 (Tokushima University Institutional Repository: 113908, CiNii: 1050865122806290304)
53.	Zhang Guodong, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Pedestrian Re-identification using Color Feature in Multi Surveillance Video, International Journal of Advances in Electronics and Computer Science, Vol.3, No.3, 11-14, 2016. (Summary) In this paper we present a system to solve the problem of moving pedestrian re-identification in surveillancevideo. Surveillance video has low-resolution, high video noise and limited monitoring scope. Our proposed framework mustdeal with several problems such as variations of illumination conditions, poses and occlusions. How to extract the robustfeature that can adapt the problems have been the task. The people of global color approaches do not change in the processof monitoring. Our paper use the color histogram as feature descriptors and choose RGB HSV and UVW for color space.Traditional histogram method extract the global color approach as the feature. The object color structure information will beneglected. We use the SPM model supplement the structure information for the histogram. The results of a test from a realsurveillance system show that our method can provide a probability of matching. (Keyword) Pedestrian Re-identification / UWV Color space / Color Histogram / SPM (Tokushima University Institutional Repository) ● Metadata: 113906 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1050865122806291456 (Tokushima University Institutional Repository: 113906, CiNii: 1050865122806291456)
54.	Sun Zhuoran, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : A Study on the Review Analysis for the Automatic Evaluation of the Product, International Journal of Management and Applied Science (IJMAS), Vol.2, No.3, 2016. (Summary) Nowadays, e-business has been never more popular and more convenient. Online merchants have flourished andmerchandize have diversified. However, its still difficult for users to quickly locate their desired products on online markets.Often times, an online shopper will evaluate ratings and comments from other users to speculate the current product. Inparticular, the easiest and the most intuitive way is to see the scores to determine the quality of the product. Therefore, if wecan automatically identify and analyze all the reviews and accurately and objectively give a score to a particular product, itwill be more efficient for users to search merchandize and for online business owners to manage the products they sell. (Keyword) e-business / Reviews / Analysis / Automatic Evaluation / management of technology (Tokushima University Institutional Repository) ● Metadata: 113907 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1050020697876158592 (Tokushima University Institutional Repository: 113907, CiNii: 1050020697876158592)
55.	Fuji Ren and Kazuyuki Matsumoto : Semi-automatic Creation of Youth Slang Corpus and Its Application to Affective Computing, IEEE Transactions on Affective Computing, Vol.7, No.2, 176-189, 2015. (Summary) This paper proposes a method to semi-automatically construct a corpus that includes Japanese youth slang called Wakamono Kotoba. The process of semi-automatic corpus construction is composed of the first step is automatic collection of example sentence, the second step is tag annotation to collected sentences, and the final step is manually modifying tag and noise reduction. In this process, there are two problems. The first problem is quality of the automatic collected corpora. The second is the accuracy of tag annotation. If the automatically annotated tags are unreliable, after all, it takes long time to modify them manually. As a solution of the first problem, we proposed a filtering method to remove meaningless sentences (noise sentences) automatically. In order to solve a second problem, we proposed an emotion estimation method that can be applied to the sentences that included youth slang and were difficult to be analyzed automatically. The result of the accuracy evaluation showed improvement in F1-Score compared to the machine learning method and confirms the effectiveness of the proposed method. (Keyword) Emotion corpus / semi-automatic corpus construction / Affective Computing / youth slang / Wakamono Kotoba (Link to Publication Site) ● Publication site (DOI): 10.1109/TAFFC.2015.2457915 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84973495084 (DOI: 10.1109/TAFFC.2015.2457915, Elsevier: Scopus)
56.	Misao Miyagawa, Kaori Katou, Yuko Yasuhara, Kazuyuki Matsumoto, Motoyuki Suzuki, Takako Takebayashi, Tetsuya Tanioka and Rozzano De Castro Locsin : Reliability Verification and Practical Effectiveness Evaluation of the Nursing Administration Analysis Formulae Based on PSYCHOMS®, Health, Vol.6, No.21, 3013-3021, 2014. (Keyword) Analysis Formulae / Nursing Administration Analysis / Psychiatric Hospital / PSYCHOMS® (Tokushima University Institutional Repository) ● Metadata: 109382 (Link to Publication Site) ● Publication site (DOI): 10.4236/health.2014.621339 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.4236/health.2014.621339 (Tokushima University Institutional Repository: 109382, DOI: 10.4236/health.2014.621339)
57.	Kazuyuki Matsumoto, Kyosuke Akita, Xielifuguli Keranmu, Minoru Yoshida and Kenji Kita : Extraction Japanese Slang from Weblog Data Based on Script Type and Stroke Count, Procedia Computer Science, Vol.35, No.2014, 464-473, 2014. (Summary) Young people commonly use slang in the texts for weblogs or Social Networking Sites. How to treat such slang words properly is one of the problems in the field of text mining. In this paper, we examined several methods to extract Japanese slang called Wakamono Kotoba, which is particularly used by young people, by focusing on its script type and stroke count. In the evaluation experiment, a high precision was obtained when we adopted script type for extraction. (Keyword) Japanese slang / Conditional Random Fields / unknown word / stroke count / script type (Link to Publication Site) ● Publication site (DOI): 10.1016/j.procs.2014.08.127 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84924135505 (DOI: 10.1016/j.procs.2014.08.127, Elsevier: Scopus)
58.	Yunong Wu, Kenji Kita and Kazuyuki Matsumoto : Three Predictions are Better than One: Sentence Multi-Emotion Analysis from Different Perspectives, IEEJ Transactions on Electrical and Electronic Engineering (TEEE), Vol.9, No.6, 642-649, 2014. (Summary) Emotion prediction has been a core task in affective computing, which aims at finding the thorough human mental states byanalyzing peoples activities. In this paper, we focus on predicting emotions in the public online blogs from different people, byextracting as many reasonable emotions for each blog sentence as possible. Concretely, we consider three different perspectivesfor analyzing the multiple emotions in a sentence: (i) predict sentence emotions by examining the emotion related topics in aglobal sense; (ii) predict the sentence emotions from the context-sensitive word emotions; and (iii) predict sentence emotions byconsidering the emotional significance in the local bag of words. We build different probabilistic models from each perspective, toseparately generate the sentence emotion probabilities. We then integrate these probabilistic models to jointly predict the emotionprobabilities. Because the component models are based on different emotional assumptions with distinct features, the integratedpredictions should predict emotions from more general perspectives and therefore yield better results. In the experiment, weemploy different evaluation criteria to compare the multi-emotion predictions from the single and integrated models. Comparedto the results in the baseline model, our bi-integrated models achieve 8.69% higher Micro F1 and 7.78% higher Macro F1 scores,on average. Moreover, our tri-integrated model acquires 10.00% higher Micro F1 and 9.19% higher Macro F1 scores than thebaseline results, which proves our assumption, and suggests interesting features in the different emotion perspectives. © 2014Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc. (Keyword) sentence emotion analysis / multi-emotion / L-LDA / CRF / LGR (Link to Publication Site) ● Publication site (DOI): 10.1002/tee.22020 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84907939400 (DOI: 10.1002/tee.22020, Elsevier: Scopus)
59.	Misao Miyagawa, Tetsuya Tanioka, Yuko Yasuhara, Kazuyuki Matsumoto, Hirokazu Ito, Motoyuki Suzuki, Fuji Ren and Rozzano De Castro Locsin : Methodology for Developing a Nursing Administration Analysis System, Intelligent Information Management, Vol.6, No.3, 118-128, 2014. (Summary) Nursing administration requires a large volume of wide-ranging information, and nurse administrators are limited in their ability to compile and analyze information for nursing administration. The purpose of this study is to create methodology for developing a nursing administration analysis system to aid nurse administrators in performing outcome analysis. In this methodology, information required for nursing administration in the PSYCHOMS? (Psychiatric Outcome Management System, registered trademark) database is analyzed according to the individual needs of nurse administrators. It features a combination of a classification method and an extraction method for obtaining quantitative and qualitative data as information required for nursing administration, and enables nurse administrators to easily obtain analysis results that they directly need. This methodology converts the time required nurse administrators to collect and organize information into time for making considerations in order to devise strategies for improving the quality of nursing care services, and can improve the quality and efficiency of nursing administration. This may lead to an increase of the quality of nursing care services at psychiatric hospitals. This methodology is highly versatile as it can be applied in information management, not only for nursing, but for the entire psychiatric hospital. (Keyword) Nursing Administration / Clinical Pathways / analysis of variance / Analysis System / PSYCHOMS® (Link to Publication Site) ● Publication site (DOI): 10.4236/iim.2014.63013 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.4236/iim.2014.63013 (DOI: 10.4236/iim.2014.63013)
60.	Nakagawa Shohei, Motoyuki Suzuki, Kazuyuki Matsumoto and Kenji Kita : Emotion Estimation Method Based on Normalization of Prosodic Features by Using Synthesis Speech, The Transactions of the Institute of Electronics, Information and Communication Engineers D, Vol.J97-D, No.3, 533-539, 2014. (Summary) 本論文では，音声からの感情推定において特徴量を正規化して識別を行う方法を提案する．従来感情識別に用いられている平均パワーやピッチといった韻律的特徴量は，感情による変化だけではなく発話内容そのものによって大きく変化する．そのため，たとえ同じ感情で発話されたとしても発話内容が異なれば異なる韻律となり，感情推定の性能低下を招き得る．そこで本論文では，平静の感情で同じ発話内容を発話した音声からも特徴量を抽出し，それとの差分に注目することで感情による特徴量の変化のみを抽出する方法を提案する．使用している5種類の特徴量(平均パワー，ピッチ等)ごとに減算による正規化，除算による正規化，正規化なし，の三つの方法で正規化し，全ての組み合わせの中で最も性能が向上する組み合わせを探索した．その結果，最適な組み合わせによる識別性能は，正規化を行わない従来法と比較して5.98%向上した．正規化法に関する分析を行ったところ，平均パワーは正規化が必要(演算は減算でも除算でも大きな差はない)であり，一方ピッチとMFCCは正規化なし，残りの二つの特徴量についてはどちらでも性能は大きくは変化しないことがわかった． (Keyword) 感情識別 / 特徴量正規化 / 合成音声 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1520009407264043136 (CiNii: 1520009407264043136)
61.	Keranmu Xielifuguli, Akira Fujisawa, Yusuke Kusumoto, Kazuyuki Matsumoto and Kenji Kita : Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features, Applied Computational Intelligence and Soft Computing, Vol.2014, 2014. (Summary) We propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant) for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method (MEM). In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features. (Keyword) image processing / information retrieval system / electroencephalogram / sentience computer (Link to Publication Site) ● Publication site (DOI): 10.1155/2014/415187 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1155/2014/415187 (DOI: 10.1155/2014/415187)
62.	Qingmei Xiao, Saito Narumi, Kazuyuki Matsumoto, Luo Xin, Yokota Yasushi and Kenji Kita : Index Compression for Audio Fingerprinting Systems Based on Compressed Suffix Array, International Journal of Information and Education Technology (IJIET), Vol.3, No.4, 455-460, 2013. (Summary) In this paper, we propose a method for index compression using a compressed suffix array. Taking advantage of the fact that the repetitive characters occur frequently in higher bits of the sorted audio fingerprint data, the proposed method compresses the index by encoding the 8-bit data sequences by Run Length Encoding. Vertical Coding is also used to compress the array, wherein the positions of the sorted data are stored. (Link to Publication Site) ● Publication site (DOI): 10.7763/IJIET.2013.V3.317 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.7763/IJIET.2013.V3.317 (DOI: 10.7763/IJIET.2013.V3.317)
63.	Fuji Ren, Changqin Quan and Kazuyuki Matsumoto : ENRICHING MENTAL ENGINEERING, International Journal of Innovative Computing, Information and Control, Vol.9, No.8, 3271-3286, 2013. (Summary) The new growing research eld of Affective Computing (AC) provides a newhorizon for quantitative analysis of human emotional states using IT techniques. In thispaper, a new academic system called nriching Mental Engineering (EME)" is proposedfor the problem of mental health from the view of engineering. EME is being establishedas an academic discipline, by being keenly aware of the poverty of the mind from whichpeople living in modern society suffer. In EME, quantitative measurement of richness ofthe mind is regarded as a central technique. This is measured from the information likesubject physiological data, textual information, behavior, and tone of voice. Meanwhile,EME also systemizes external stimuli by an emotional energy function. The emotionenergy function is proposed to calculate a person's emotional stimuli at a certain pointfrom factors like choice of words, voice, facial expressions, physiological information, andbehavior. Furthermore, an application of EME is illustrated through an analysis of thedepressive tendencies in blogs. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84880108523 (Elsevier: Scopus)
64.	Qingmei Xiao, Daito Yusaku, Kazuyuki Matsumoto, Motoyuki Suzuki and Kenji Kita : Fast Search Method for Audio Fingerprinting Systems Based on Query Multiplexing, IEEJ Transactions on Electronics, Information and Systems, Vol.132, No.9, 1481-1487, 2012. (Summary) In music information retrieval, a huge search space has to be explored because a query audio clip can start at any position of any music in the database, and also a query is often corrupted by highly significant noise and distortion. Audio fingerprints have attracted much attention recently for providing compact representation of the perceptually relevant parts of audio signals. In this paper, we propose an extremely fast method of exploring a huge hamming space for audio fingerprinting systems. The effectiveness of our method has been evaluated by experiments using databases of 8,740 real songs and 800 artificially corrupted and 268 real queries. (Keyword) music information retrieval / audio fingerprint / hamming space / query multiplexing (Link to Publication Site) ● Publication site (DOI): 10.1541/ieejeiss.132.1481 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282679584466688 ● Search Scopus @ Elsevier (DOI): 10.1541/ieejeiss.132.1481 (DOI: 10.1541/ieejeiss.132.1481, CiNii: 1390282679584466688)
65.	Zhang Le, Kazuyuki Matsumoto, Motoyuki Suzuki and Kenji Kita : Skelton Pruning Based on the Total Bisector Angle of the End Branches, IEEJ Transactions on Electronics, Information and Systems, Vol.132, No.5, 750-758, 2012. (Summary) Skeletonization is an important technique for shape representation, but the skeleton's high sensitivity to boundary noise hampers its application to automatic shape matching. In this paper, we propose a linear-time skeleton pruning algorithm based on the total bisector angle of the end branches. The main idea of the proposed algorithm is to iteratively delete the end branches having the smallest total bisector angle. Since the total bisector angle considers both the local and the global skeleton information, the proposed algorithm removes all redundant branches generated by boundary noise and obtains the ideal skeleton. The experimental results show that the proposed algorithm is highly adaptable, which means that under the same threshold conditions, the pruned skeletons obtained by our proposed algorithm are in accord with human visual perception for most shapes in the MPEG-7 and Tari 56 datasets. Consequently, the pruned skeletons can be applied to automatic shape matching. (Keyword) Skelton / Skelton Pruning / Total Bisector Angle / End Branch (Link to Publication Site) ● Publication site (DOI): 10.1541/ieejeiss.132.750 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282679586212736 ● Search Scopus @ Elsevier (DOI): 10.1541/ieejeiss.132.750 (DOI: 10.1541/ieejeiss.132.750, CiNii: 1390282679586212736)
66.	Kazuyuki Matsumoto, Kenji Kita and Fuji Ren : Emotional Vector Distance Based Sentiment Analysis of Wakamono Kotoba, China Communications, Vol.9, No.3, 87-98, 2012. (Summary) In this paper, we propose a method for estimating emotion in Wakamono Kotoba that were not registered in the system, by using Wakamono Kotoba example sentences as features. The proposed method applies Earth Mover's Distance(EMD) to vector of words. As a result ofthe evaluation experiment using 14,440 sentences, higher estimation accuracy is obtained by considering emotional distance between words - an approach that had not been used in the conventional research - than by using only word importance value. (Keyword) Wakamono Kotoba / sentiment analysis / emotion estimation / Earth Mover's Distance (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84861787581 (Elsevier: Scopus)
67.	Yunong Wu, Kenji Kita, Fuji Ren, Kazuyuki Matsumoto and XIN KANG : Exploring the Importance of Modification Relation for Emotional Keywords Annotation and Emotion Types Recognition, International Journal of Intelligent Engineering and Systems, Vol.4, No.4, 19-26, 2011. (Summary) In this study, we make a scheme to explore the importance of modification relation for the emotional keywords annotation and emotion types recognition. We extract three modification features which are degree words,negative words and conjunctions from the Chinese emotion corpus named Ren-CECps. Beside word and part-of speech,three modification relations are adopted as feature in this study. We have carried out eight experiments with different feature sets for emotional keywords annotation and emotion types recognition in sentence level. Eight basic emotion types have been selected and Conditional Random Fields have been employed as the algorithm. In the part of evaluation, we demonstrate the importance of the modification features and our experiment results show the effectiveness of the modification features for improving the performance of emotional keywords annotation and emotion types recognition. (Keyword) Emotional Keywords Annotation / Emotion types recognition / Modification relation / CRFs (Link to Publication Site) ● Publication site (DOI): 10.22266/ijies2011.1231.03 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84859614924 (DOI: 10.22266/ijies2011.1231.03, Elsevier: Scopus)
68.	Kazuyuki Matsumoto and Fuji Ren : Estimation of word emotions based on part of speech and positional information, Computers in Human Behavior, Vol.27, No.5, 1553-1564, 2011. (Summary) Recently, studies on emotion recognition technology have been conducted in the fields of natural language processing, speech signal processing, image data processing, and brain wave analysis, with the goal of letting the computer understand ambiguous information such as emotion or sensibility. This paper statistically studies the features of Japanese and English emotional expressions based on an emotion annotated parallel corpus and proposes a method to estimate emotion of the emotional expressions in the sentence. The proposed method identifies the words or phrases with emotion, which we call emotional expressions, and estimates the emotion category of the emotional expressions by focusing on the three kinds of features: part of speech of emotional expression, position of emotional expression, and part of speech of the previous/next morpheme of the target emotional expression. (Keyword) Emotion recognition / Emoton corpus / Sensibility (Link to Publication Site) ● Publication site (DOI): 10.1016/j.chb.2010.10.028 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.chb.2010.10.028 (DOI: 10.1016/j.chb.2010.10.028)
69.	Kazuyuki Matsumoto, Hidemichi Sayama, Yusuke Konishi and Fuji Ren : Analysis of Wakamono Kotoba Emotion Corpus and Its Application in Emotion Estimation, International Journal of Advanced Intelligence (IJAI), Vol.3, No.1, 1-24, 2011. (Summary) Recently, there is a lot of research that aims to estimate emotion from text. The meaningsof linguistic expressions used in daily life vary depending on the context in which theyare used. That is to say, the information they contain presents ambiguities. Especiallythe so-called Wakamono Kotoba, Japanese language used by young people containssemantic ambiguities. Such words are usually not included in the existing dictionaries,making the meanings of these words difficult to be recognized. In this research projectwe proposed a method to estimate emotion from sentences that include Wakamono Kotobaby using statistical learning methods such as Na¨ıve Bayes method and Accumulationmethod. The existing research usually focused on learning methods using word or wordN-gram as features. However, such word-based features are insufficient to process WakamonoKotoba because Wakamono Kotoba often cannot be recognized as one semanticword by morphological analysis. In this paper we describe how we constructed the linguisticresource of Wakamono Kotoba emotion corpus to be used for emotion recognitionand introduce the features we obtained from statistical analysis. Our Wakamono Kotobaemotion corpus includes Japanese words used by young people to express emotion. Thesewere mainly gathered from Weblogs that were written by young people from their teensto their twenties. (Keyword) Emotion Recognition / Wakamono Kotoba / Emotion Corpus
70.	Kazuyuki Matsumoto, Junko Minato and Fuji Ren : Retrieval Method of Example Sentence for Intelligent English Composition Support System, Information : an International Interdisciplinary Journal, Vol.12, No.2, 377-386, 2009. (Summary) Recently the opportunity of reading and writing in English has been greatly increasing. We focused on supporting the Japanese to write English sentence by indicating appropriate example sentences. This paper proposed a method to retrieve such target example sentences by using domain categories and keywords or directly inputting the Japanese sentence.The result of the experiment to evaluate the precision of example sentence retrieval indicated that our technique exceeded the conventional technique by more than 20%.
71.	Kazuyuki Matsumoto, Tetsuya Tanioka, Kyoko Osaka, Kawamura Ai, Shu-ichi Ueno, Fuji Ren, Takasaka Yoichiro, Barnard Alan, Rozzano De Castro Locsin and Omori Mitsuko : Developing the Method of Server Controlled Outcomes Management and Variance Analysis, Electronic Notes in Theoretical Computer Science, Vol.225, 221-237, 2009. (Summary) This paper is to describe a way to develop the Psychoms process for mental health patient management and variance analysis system has been developed using artificial intelligence. Although it is agreed that there is a need for clinical pathway variance analysis, methods for creating a system are less well defined. The procedure and systematic process described aims to improve patients' quality of life through consistent and timely care. Ultimately, its potential influence is to assist in the improvement of quality health care services. This paper illustrates a method of outcomes management and variance analysis as the prospective development of future research. (Keyword) Outcome Management / variance analysis / nursing administration / team care / artificial intelligence (Link to Publication Site) ● Publication site (DOI): 10.1016/j.entcs.2008.12.076 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1016/j.entcs.2008.12.076 (DOI: 10.1016/j.entcs.2008.12.076)
72.	Junko Minato, Kazuyuki Matsumoto, Fuji Ren, Seiji Tsuchiya and Shingo Kuroiwa : Evaluation of Emotion Estimation Methods Based on Statistic Features of Emotion Tagged Corpus, International Journal of Innovative Computing, Information and Control, Vol.4, No.8, 1931-1941, 2008. (Summary) Development of information technology has been increasing the chance of interaction between human and computer. Computer used to be a tool and its development had been mainly emphasized on the speed of information processing. However, by understanding not only semantic information but also ambiguous information such as emotion, computer can be a versatile partner for human that can detect emotional state of a person and can response appropriately the emotion. As a primary study of text-based emotion estimation by computer we focused on statistically analyzing the relationship between word emotion and sentence emotion based on the originally created emotion tagged corpus then propose two emotion estimation methods according to the obtained statistic features. The proposed methods are evaluated using a prototype system. (Keyword) Corpus analysis / Affective computing / Statistic analysis (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-63749121764 (Elsevier: Scopus)
73.	Kazuyuki Matsumoto, Fuji Ren, Shingo Kuroiwa and Seiji Tsuchiya : Emotion Estimation Algorithm Based on Interpersonal Emotion Included in Emotional Dialogue Sentences, Lecture Notes in Computer Science, Vol.4827, 1035-1045, 2007. (Summary) Emotion recognition aims to make computer understand ambiguous information of human emotion. Recently, research of emotion recognition is actively progressing in various fields such as natural language processing, speech signal processing, image data processing or brain wave analysis. We propose a method to recognize emotion in dialogue text by using originally created Emotion Word Dictionary. The words in the dictionary are weighted according to the occurrence rates in the existing emotion expression dictionary. We also propose a method to judge the object of emotion and emotion expressivity in dialogue sentences. The experiment using 1,190 sentences proved about 80% accuracy. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-540-76631-5_99 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-38149123913 (DOI: 10.1007/978-3-540-76631-5_99, Elsevier: Scopus)
74.	Kazuyuki Matsumoto, Kenichi Mishina, Fuji Ren and Shingo Kuroiwa : Emotion Estimation Algorithm based on Emotion Occurrence Sentence Pattern, Journal of Natural Language Processing, Vol.14, No.3, 239-271, 2007. (Summary) 近年の情報処理技術の発達に伴い, 従来の情報処理の分野ではほとんど取り扱われなかった人間の感性をコンピュータで処理しようとする試みが盛んになってきた. 擬人化エージェントや感性ロボットが人のように振舞うためには, 人間が表出する感情を認識し, 自ら感情を表出することが必要である. 我々は, 感性ロボットに応用するための感情認識技術について研究している. 自然言語会話文からの感情推定を行う試みは, 多くの場合, 表面的な感情表現のみに絞つて行われてきた. しかし, 人間の発話時には常に何らかの感情が含まれていると考えられる. そこで, 本稿では, 感情語と感情生起事象文型パターンに基づいた感情推定手法を提案し, 実験システムを構築する. そして, 本手法の有効性を調べるため, シナリオ文を対象にその評価実験を行った. (Keyword) Sensibility information processing / Emotion recognition / sentience computer / natural language processing / Sensibility information processing / Emotion recognition / Sensibility computer (Link to Publication Site) ● Publication site (DOI): 10.5715/jnlp.14.3_239 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001204476728832 ● Search Scopus @ Elsevier (DOI): 10.5715/jnlp.14.3_239 (DOI: 10.5715/jnlp.14.3_239, CiNii: 1390001204476728832)
75.	Kazuyuki Matsumoto, Fuji Ren and Shingo Kuroiwa : Emotion Estimation System based on Emotion Occurrence Sentence Pattern, Lecture Notes in Computer Science, Vol.4114, 902-911, 2006. (Summary) The approach of emotion estimation from the conventional text was for estimating superficial emotion expression mainly. However emotions may be included in humans utterance even if emotion expressions are not in it. In this paper, we proposed an emotion estimation algorithm for conversation sentence. We gave the rules of emotion occurrence to 1616 sentence patterns. In addition, we developed a dictionary which consisted of emotional words and emotional idioms. The proposed method can estimate emotions in a sentence by matching the sentence pattern of emotion occurrence and the rule. Furthermore, we can get two or more emotions included in the sentence by calculating emotion parameter. We constructed the experiment system based on the proposed method for evaluation. We analyzed weblog data including 253 sentences by the system, and conducted the experiment to evaluate emotion estimation accuracy. As a result, we obtained the estimation accuracy of about 60 %. (Keyword) emotion estimation / emotion occurrence sentence pattern (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-540-37275-2_113 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-33749564986 (DOI: 10.1007/978-3-540-37275-2_113, Elsevier: Scopus)

Academic Letter:

1.	Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Analyzing Transformers via Value Matrices, Transactions of the Japanese Society for Artificial Intelligence, Vol.38, No.2, 2023. (Keyword) Transformer / value matrix / word embedding (Link to Publication Site) ● Publication site (DOI): 10.1527/tjsai.38-2_C-MB7 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390013795251121280 ● Search Scopus @ Elsevier (DOI): 10.1527/tjsai.38-2_C-MB7 (DOI: 10.1527/tjsai.38-2_C-MB7, CiNii: 1390013795251121280)

Review, Commentary:

1.	Fujisawa Akira, Kazuyuki Matsumoto, Okumura Noriyuki, Minoru Yoshida and Kenji Kita : Challenge to ASCII Art - An Image Feature-Based Approach -, Journal of Japanese Society for Artificial Intelligence, Vol.32, No.3, 364-370, May 2017. (Keyword) ASCII arat / image feature / non-verbal expression / emoticon / histograms of oriented gradients (Tokushima University Institutional Repository) ● Metadata: 113912 (Tokushima University Institutional Repository: 113912)

Proceeding of International Conference:

1.	Tang XiongFei and Kazuyuki Matsumoto : Emoji Recommendation Method Focusing on the Distribution of Emotion in the Context, Proceedings of the 2024 20th International Conference on Natural Computation, Fuzzy Systems, and Knowledge Discovery, Guangzhou, (Summary) This study performs sentiment analysis on tweets with emojis for an emoji recommendation model. Automatically collecting posts with emojis from social media, a dataset was assembled. Training was conducted on the WRIME dataset, which includes emotion intensity labels for social media texts, whereby a BERT-based sentiment classification model was used to analyze the emotion distribution of the context for each emoji in social media posts. This analysis determines which emoji is assigned to the text by the emotion distribution of the context, thereby handling complex sentiments expressed in different contexts, which is a more flexible emoji recommendation method than using the sentiment information of emojis alone. Experimental results showed that clustering emojis based on the emotion values of their context achieved the higher recommendation accuracy. (Keyword) emoji / recommendation / social networking site / emotion recognition / k-means clustering / BERT
2.	Haruto Uda, Kazuyuki Matsumoto and Minoru Yoshida : Text Data Augmentation Method Using Filtering Indicators based on Multiple Perspectives, Proceedings of the 38th Pacific Asia Conference on Language, Information and Computation, 1-10, Dec. 2024. (Summary) The widespread use of social networking services (SNS) has made it possible to collect a wide variety of text data on a large scale. Text data posted on SNS contain many broken expressions, especially abbreviations and colloquial expressions. In order to utilize such data as a resource for natural language processing, annotation of the data, assignment of class labels, etc. become issues. In general, because manual annotation is costly, artificial data augmentation and semi-automation of label assignment are often used as a countermeasure against data shortages. In this study, we propose a method for efficiently preparing large-scale, high-quality labeled text data for machine learning by applying evaluation indicators from multiple perspectives to the data generated by data augmentation methods. The goal is to improve the prediction accuracy of the model by adding the augmented data to the training data. Specifically, the proposed method sets thresholds for the semantic similarity based on the vector of BERT between the original text and the augmented text, the degree of change by BLEU, and the change in attention by Attention, respectively, and deletes data that do not satisfy the threshold conditions. Since the number of augmented data also affects learning accuracy, the number of data is also addressed by adding it to the evaluation indicators. Evaluation experiments on emotionlabeled datasets show that the proposed method achieves higher Accuracy than the method that simply augments the data using Easy Data Augmentation. (Keyword) easy data augmentation / multiple perspectives / imbalanced data / small data / sentiment analysis
3.	Toshiki Takanabe, Kotaro Kashihara, Kazuyuki Matsumoto, Keita Kiuchi, XIN KANG, Ryota Nishimura and Manabu Sasayama : Multimodal Emotion Recognition and Dataset Construction in Online Counseling, Proceedings of the 38th Pacific Asia Conference on Language, Information and Computation, 1-9, Dec. 2024. (Summary) In this study, we developed a multimodal dataset and performed emotion recognition experiments.The dataset includes objective emotion labels derived from online counseling videos. Five individuals were asked to predict the emotions of the person speaking in each counseling video and to assign emotion labels. Each video was evaluated by positioning a cursor on Russell's circumplex model, where the xaxis represents emotional valence(pleasantness-unpleasantness) and the yaxis represents arousal levels. To assess the inter-rater reliability of these evaluations, we calculated Fleiss' kappa. Using the constructed dataset, we conducted an emotion recognition experiment employing a Hybrid Fusion approach. Specifically, weused emotion recognition results from pyfeat as features from images, acoustic features from wav2vec2.0 as features from speech and text-embedding-3 as features from language.When the acoustic features were weighted 0.4, the facial features 0.3, and the linguistic features 0.3, the result for the 16 emotion classifications was the most accurate, with a score of 0.4521. (Keyword) multimodal / emoction recognition / dataset / online counseling
4.	Mori Daiki, Kazuyuki Matsumoto, XIN KANG, Manabu Sasayama and Keita Kiuchi : SMACS: Stress Management AI Chat System, Proceedings of the 16th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management (IC3K 2024), Vol.2, 167-174, Porto, Nov. 2024. (Summary) The purpose of this study is to develop a stress management AI chat system that can connect users who want mental health care with counselors. By means of this chat system, a conversational AI based on a large language model (LLM) will collect data on the user's stressors through text chats with the user. The system is personalized to the user based on the collected data. This paper describes the nature of the data collected in the preliminary experiment conducted in March 2024 and the results of its analysis, and discusses considerations for the main experiment to be conducted after July 2024. The preliminary experiment was conducted with 11 students over a 3-week period. Discuss the distribution of the data collected and the issues involved in building a model for predicting stress levels. (Keyword) conversational AI / mental health / mental healthcare / stress detection / Large Language Models / chat applications / natural language processing
5.	XIN KANG, Zhang Zhifei, Jiazheng Zhou, Yunong Wu, Shi Xuefeng and Kazuyuki Matsumoto : TMAK-Plus at SIGHAN-2024 dimABSA Task: Multi-Agent Collaboration for Transparent and Rational Sentiment Analysis, The 62nd Annual Meeting of the Association for Computational Linguistics (ACL 2024), 1-8, Bangkok, Aug. 2024.
6.	Wang Linhuang, XIN KANG, Ding Fei, Haitao Yu, Yunong Wu, Kazuyuki Matsumoto, Nakagawa Satoshi and Fuji Ren : Self Decoupling-Reconstruction Network for Facial Expression Recognition, International Joint Conference on Neural Networks (IJCNN 2024), 1-8, Yokohama, Jul. 2024.
7.	Yupu Liu and Kazuyuki Matsumoto : Enhancing Multimodal Tweet Analysis Accuracy through Integration of CLIP Model and Multi-layer Attention Mechanism, The 10th International Forum on Advanced Technologies 2024, Mar. 2024. (Summary) In this study, we aim to conduct sentiment analysis and identify popular content on Twitter using a multimodal approach that combines the CLIP model with a multi-layer attention mechanism. By comprehensively analyzing both text and image information shared on Twitter, we can precisely grasp the trends in emotions and indicators of popularity. This enables various applications such as analysis of social media trends, evaluation of influencers' impact, and even prediction of market trends. In particular, identifying popular tweets based on the number of likes is expected to contribute to understanding the mechanisms of viral marketing and information dissemination. (Keyword) CLIP / multimodal analysis / attention mechanism
8.	Ryuichi Tanioka, Yoshihiro Kai, Kazuyuki Matsumoto, Kensaku Takase, Kyoko Osaka and Tetsuya Tanioka : Upper Limb Motor Velocity Compared Between Patient with Schizophrenia and Healthy Subject: A Case Study, 6TH SPUP INTERNATIONAL INTERDISCIPLINARY RESEARCH CONFERENCE, Tuguegarao City ,Philippines, Aug. 2023. (Keyword) angular velocity / motor velocity / patient with schizophrenia / upper limb
9.	Kazuyuki Matsumoto, Minoru Yoshida and Ryota Kishima : Analysis of Emotional Changes in Twitter Users by Personality Type Before and After the Outbreak of the COVID-19 Pandemic, Proceedings of the 202319th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (FSKD), Harbin, Jul. 2023. (Summary) Three years have passed since the outbreak of the novel coronavirus pandemic at the end of 2019, and the situation continues to change daily as non-pharmaceutical interventions and associated restrictions are gradually being eased. Even now, drastic changes in aspects of daily living continue to have significant impacts in terms of physical and mental stress on many people. In this study, we analyzed changes in Twitter users comments, thoughts, and actions by conducting sentiment analysis of the content of tweets posted before and after the outbreak of the pandemic. We extracted characteristic expressions and conducted a comparative analysis. In addition, we examined changes in emotions according to users personality types. (Keyword) Twitter / pandemic / emotion changes / personality (Link to Publication Site) ● Publication site (DOI): 10.1109/ICNC-FSKD59587.2023.10280850 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/ICNC-FSKD59587.2023.10280850 (DOI: 10.1109/ICNC-FSKD59587.2023.10280850)
10.	Toshiya Akiyama, Kazuyuki Matsumoto, Kyoko Osaka, Ryuichi Tanioka, Yuko Yasuhara, Hirokazu Ito, Gil Soriano, Allan Paulo Blaquera, Yoshihiro Kai and Tetsuya Tanioka : A pilot case study for developing a software for human emotion recognition using multimodal data, 2023 IEEE/SICE International Symposium on System Integration (SII), 1041-1045, WEB, Jan. 2023. (Keyword) Medical Systems / Human-Robot/System Interaction / Multi-Modal Perception (Link to Publication Site) ● Publication site (DOI): 10.1109/SII55687.2023.10039248 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/SII55687.2023.10039248 (DOI: 10.1109/SII55687.2023.10039248)
11.	Shohei Noguchi, Kazuyuki Matsumoto, Minoru Yoshida, Ryota Nishimura and Kenji Kita : Extraction of keywords from disease-fighting blogs by fine-tuning the T5 model, 2022 6th International Conference on Natural Language Processing and Information Retrieval, Dec. 2022. (Summary) To measure lifestyle-related diseases such as heart disease and diabetes, which are on the increase, information on patients' lifestyles is necessary. With the growth of the Internet culture in recent years, a great deal of valuable information about patients' long fight against the disease, including lifestyle and condition changes, is now being written in the form of blogs and tweets. In this study, keywords related to changes in health status are extracted from "disease-fighting blogs" using Named Entity Recognition system, which is fine-tuned the "T5 Model", one of the latest deep learning and natural language processing models. This method can be applied to a system that informs patients and health care professionals how their lifestylehabits affect their health status. (Keyword) disease-fighting blog / named entity recognition / T5
12.	Daiki Mori, Kazuyuki Matsumoto, Manabu Sasayama, Minoru Yoshida and Kenji Kita : Emotion and personality estimation of utterances in a dialogue model reflecting personality, Proceedings of 4th International Conference on Technological Competency as Caring in Nursing and Health Sciences 2022, 105, Online, Dec. 2022. (Summary) One of the factors that enable humans to interact with AI in a friendlier manner is personality, and since the personality required of AI varies depending on the situation and context, a mechanism is needed that allows AI to switch between different personalities depending on the person with whom he or she is interacting. In this study, we use a Japanese language Transformer Encoder-decoder dialogue model (Japanese-dialog-transformers) and aim to construct a dialogue model that reflects the personality of the target person by fine-tuning the interview dialogue data. By analyzing the output of the proposed dialogue models using the MBTI estimation model and the sentiment analysis model, we evaluate the consistency of the personality and discuss the differences and similarities among the dialogue models. (Keyword) natural language processing / dialogue model / artificial intelligence / deep learning
13.	Kazuyuki Matsumoto : Objective Analysis Techniques for Descriptive Answer Sentences in Questionnaires Using Natural Language Processing and Machine Learning, 4TH INTERNATIONAL CONFERENCE ON TECHNOLOGICAL COMPETENCY AS CARING IN NURSING AND HEALTH SCIENCES 2022, Online, Dec. 2022. (Summary) When analyzing descriptive answer sentences in the questionnaire survey, there is the problem that the subjectivity of theanalyst may enter into the qualitative analysis results. For example, whether or not sentences corresponds to expressions suchas the spread of the new coronavirus infection as the cause of spending less time with family in a freestyle descriptiveanswer will vary depending on the skill and perspective of each analyst. Natural language processing technology and machinelearning algorithms enable objective analysis and save the human and time costs of analyzing large-scale data sets. However,the most important issue is how to set the extraction and classification criteria, and the performance of the created algorithmsand models depends on the quality of the training data prepared. Recently, fine-tuned a large-scale generic language modelcreated by unsupervised learning to adapt it to the task is often used in the field of natural language processing. The use ofgeneral-purpose language models also enables efficient execution of freestyle sentences clustering and visualization. I willexplain the design of annotation guidelines for freestyle sentences in training data creation, preprocessing, and task adaptationof general-purpose language models, introducing examples from previous studies. I will also briefly describe the deep learningtechnique Transformer networks, compare it with conventional probabilistic models (Hidden Markov Models, ConditionalRandom Fields, etc.) and discuss its advantages in freestyle sentence questionnaire analysis. (Keyword) natural language processing / artificial intelligence / classification / deep learning
14.	Junya Furutani, XIN KANG, Keita Kiuchi, Ryota Nishimura, Manabu Sasayama and Kazuyuki Matsumoto : Learning a Bimodal Emotion Recognition System Based on Small Amount of Speech Data, 2022 8th International Conference on Systems and Informatics (ICSAI), 160-164, Kunming, China, Dec. 2022. (Summary) This paper presents a bimodal emotion recognition system based on the voice and text information using small amount of speech data. Specifically, speech is divided into voice and text to learn emotion classifiers for each modal. The probabilities obtained from these emotion classifiers are weighted based on Mehrabians rule and summed up for each emotion to calculate the final score for bimodal emotion recognition. To create a highly accurate system while solving the problem that there are few Japanese speech data with emotion labels, we propose a novel data augmentation method and employ a transfer learning approach based on a pre-trained VGG16 model and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model on the tweets. In order to prove the effectiveness of the proposed method, we revealed the recognition results for 7 emotional states, i.e., anger, sadness, joy, fear, surprise, disgust, and neutral. The experiment result suggested that our novel data augmentation method improved the accuracy and that bimodal predictions based on voice and text-based outperformed the single-model predictions. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICSAI57119.2022.10005454 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85146960025 (DOI: 10.1109/ICSAI57119.2022.10005454, Elsevier: Scopus)
15.	Tomoki Kusunose, XIN KANG, Keita Kiuchi, Ryota Nishimura, Manabu Sasayama and Kazuyuki Matsumoto : Facial Expression Emotion Recognition Based on Transfer Learning and Generative Model, 2022 8th International Conference on Systems and Informatics (ICSAI), 107-112, Kunming, China, Dec. 2022. (Summary) Facial expression emotion recognition has been a popular research topic, which played an important role in assisting the natural human-machine conversation. The conventional method for emotion estimation from facial expressions is to learn a CNN-based image classification model from scratch, However, learning such model requires a large number of labeled facial expression images, which is still a limited resource until now. To solve this problem, we propose a data augmentation method based on StyleGAN2 to generate artificial expression images with respect to seven emotions and use them as the additional training data. We further train an expression emotion recognition model based on a VGG16 network through transfer learning. In this research, we proposed a method using transfer learning and augmented images of facial expressions using trained VGG16 and StyleGAN2 and conducted experiments to achieve higher recognition accuracy for racial expression emotion recognition. Our experiment based on the CFEE dataset suggested that an emotion recognition accuracy of 75.10% could be obtained through transfer learning and the accuracy could further improved to 82.04% with the augmented expression images. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICSAI57119.2022.10005478 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85146960739 (DOI: 10.1109/ICSAI57119.2022.10005478, Elsevier: Scopus)
16.	Yukari Hisaka, Hirokazu Ito, Yuko Yasuhara, Kazuyuki Matsumoto, Blaquera Paulo Allan, Takase Kensaku and Tetsuya Tanioka : Difficulties in Nursing Practice Content and its Reasons for Patients with Acute Stroke, 4TH INTERNATIONAL CONFERENCE ON TECHNOLOGICAL COMPETENCY AS CARING IN NURSING AND HEALTH SCIENCES 2022, WEB, Dec. 2022. (Keyword) Patients with acute stroke / Nursing practice / Morphological analysis
17.	Akiyama Toshiya, Soriano Gil, Blaquera Paulo Allan, Yuko Yasuhara, Hirokazu Ito, Kazuyuki Matsumoto, Zhao Yueren, Kyoko Osaka, Tanioka Ryuichi, Kai Yoshihiro and Tetsuya Tanioka : Inter-Rater Reliability Between the Results of Human Judgments of Emotions and those of Empathy, 4TH INTERNATIONAL CONFERENCE ON TECHNOLOGICAL COMPETENCY AS CARING IN NURSING AND HEALTH SCIENCES 2022, WEB, Dec. 2022. (Keyword) Empath, Emotions / Human judgement / Inter-rater reliability
18.	Reishi Amitani, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Affective Analysis and Visualization from Posted Text, Replies, and Images for Analysis of Buzz Factors, Frontiers in Artificial Intelligence and Applications (FAIA), Fuzzy Systems and Data Mining VIII, Vol.358, 191-203, Nov. 2022. (Summary) In this study, we propose a method to visualize the factors that contribute to the buzz phenomenon triggered by Twitter posts. The analysis included tweets, images, and replies. Replies are after-the-fact responses posted in response to a posted tweet and therefore cannot be used to predict buzz phenomena. Therefore, they cannot be used to predict the buzz phenomena. In this study, the tweet body, images, and reply text were feature vectors, and an affective analysis model was constructed. Visualization of the relationship between the sensibility features output from this model and the number of RTs and likes (echo index), which represent the scale of the buzz, will be useful for analyzing the factors behind the popularity. Consequently, the subjective sensibility information with the most likes also tended to have a higher degree of similarity among the sensibility vectors. (Keyword) Tweet analysis / natural language processing / trend analysis (Link to Publication Site) ● Publication site (DOI): 10.3233/FAIA220384 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85142130952 (DOI: 10.3233/FAIA220384, Elsevier: Scopus)
19.	Motonobu Yoshida, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : A System to Correct Toxic Expression with BERT, Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KEOD, 92-97, Valletta, Oct. 2022. (Summary) This paper describes a system for converting posts with toxic expression on social media, such as those containing slander and libel, into less-toxic sentences. In recent years, the number of social media users as well as the cases of online flame wars has been increasing. Therefore, to prevent flaming, we first use a prediction model based on Bidirectional Encoder Representations from Transformers (BERT) to determine whether a sentence is likely to be flamed before it is posted. The highest classification accuracy recorded 82% with the Japanese Spoken Language Field Adaptive BERT Model (Japanese Spoken BERT model) as a pre-trained model. Then, for sentences that are judged to be toxic, we propose a system that uses BERTs masked word prediction to convert toxic expressions into safe expressions, thereby converting them into sentences with mitigated aggression. In addition, the BERTScore is used to quantify whether the meaning of the converted sentence has changed in meaning compared to the original sentence and evaluate whether the modified sentence is safe while preserving the meaning of the original sentence. (Keyword) Toxic Expression / BERT / classification / Text Crrection / Flame War (Link to Publication Site) ● Publication site (DOI): 10.5220/0011586100003335 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85146200806 (DOI: 10.5220/0011586100003335, Elsevier: Scopus)
20.	Daichi Kawahara, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Music Video Search System Based on Comment Data and Lyrics, Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery(Lecture Notes on Data Engineering and Communications Technologies; LNDECT), Vol.153, 1125-1135, Jul. 2022. (Summary) Many people currently use video-sharing services such as YouTube. Keyword search is prevalent in these services. It can be hard to find a video that matches the users' interests using keyword search unless appropriate words are used. In this study, we propose a method for retrieving music videos with similar impressions by analyzing comment data from YouTube viewers and music lyrics. The proposed method converts comments and lyrics into vectors using Word2Vec, and music videos with similar impressions are retrieved using fuzzy c-means clustering According to the mean reciprocal rank (MRR) scores, it was clear that the output of the music videos had the same impression within the top three songs. (Keyword) artificial intelligence / music video search / lyric / fuzzy c-means (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-031-20738-9_122 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85147845007 (DOI: 10.1007/978-3-031-20738-9_122, Elsevier: Scopus)
21.	Naomichi Tabuchi, Kazuyuki Matsumoto, Minoru Yoshida, Ryota Nishimura and Kenji Kita : Analysis of Lifestyle Habits from Weblogs of Patients with Intractable Diseases Using Deep Learning, Proceedings of International Conference on Electrical, Computer and Energy Technologies (ICECET), Jul. 2022. (Summary) In recent years, patient-centered medicine has become widely accepted. To keep a record of their struggles,patients sometimes publish their diaries (disease blogs) on weblogs to be read by an unspecified number of people. In particular, writing a blog may be helpful in the case of intractable diseases of which there are few cases, and patients often feel isolated because they know few people with the same disease. In this study, we used deep learning technology to analyze the blogs written by patients with intractable diseasesand to determine how they live their daily lives and their feelings. If their lifestyles are understood, the relationship between the physical condition and mental changes of people fighting intractable diseases can be determined, and a system that shows behavioral guidelines for new users fighting similar intractable diseases can be built. (Keyword) lifestyle habits / intractable disease / deep learning (Link to Publication Site) ● Publication site (DOI): 10.1109/ICECET55527.2022.9872887 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85138876019 (DOI: 10.1109/ICECET55527.2022.9872887, Elsevier: Scopus)
22.	Naomichi Tabuchi, Kazuyuki Matsumoto, Minoru Yoshida, Ryota Nishimura and Kenji Kita : Risk Analysis of Developing Lifestyle-related Diseases based on the Content of Social Networking Service Posts, Innovation in Medicine and Healthcare, Part of the Smart Innovation, Systems and Technologies book series, Vol.308, 63-72, Jun. 2022. (Summary) Japans leading causes of death are cancer, cardiovascular disease, and diabetes, which account for approximately 60% of all deaths. All these diseases are closely related to lifestyle habits such as diet, exercise, and sleep and are called lifestyle-related diseases. Lifestyle-related diseases are characterized by a lack of subjective symptoms, making it difficult to notice the deterioration of the disease. In this study, we analyzed tweets related to diet, exercise, physical condition, and mental state from the contents posted by users on social networking services (SNS) to construct a system that can prevent lifestyle-related diseases more simply. We found that the results of this work can lead to the construction of a system than can detect risk factors leading to the development of lifestyle-related diseases, from the contents of users posts. (Keyword) risk analysis / social networking service / lifestyhle-related diseases (Link to Publication Site) ● Publication site (DOI): 10.1007/978-981-19-3440-7_6 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85135061563 (DOI: 10.1007/978-981-19-3440-7_6, Elsevier: Scopus)
23.	Manabu Sasayama and Kazuyuki Matsumoto : Annotation and Evaluation of Utterance Intention Tag for Interview Dialogue Corpus, Proceedings of 2021 5th International Conference on Natural Language Processing and Information Retrieval, Dec. 2021. (Summary) In this paper, we proposed the utterance intention tags for an interview dialogue corpus. We constructed an interview dialogue corpus with the tags we designed. Three or five annotators annotated the tags to the interview dialogue corpus (the total number of 49999 utterances) consisting of 30 dialogues. We conducted an evaluation experiment using Fleiss's kappa value to evaluate the reliability of the proposed tags. When three annotators annotated 18 different tags to the corpus, we obtained the kappa value of 0.55. (Keyword) interview dialogue / corpus / corpus construction / annotation / speech intention (Link to Publication Site) ● Publication site (DOI): 10.1145/3508230.3508236 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1145/3508230.3508236 (DOI: 10.1145/3508230.3508236)
24.	Taiga Kirihara, Kazuyuki Matsumoto, Manabu Sasayama, Minoru Yoshida and Kenji Kita : Topic Segmentation for Interview Dialogue System, Proceedings of 2021 5th International Conference on Natural Language Processing and Information Retrieval, Online, Dec. 2021. (Summary) In this study, topic segmentation was performed by referring to the interview dialogue corpus. Utterance intention tags were added to the existing interview dialogue corpus, and uttered sentences were vectorized using BERT, Sentence BERT, and Distil BERT. In addition, topic classification was performed using the utterance intention tags and the features of the preceding and following uttered sentences. Consequently, the greatest accuracy was achieved when the utterance intention tag was used with DistilBERT (Keyword) interview dialogue / neural networks / speech intention / DistilBERT (Link to Publication Site) ● Publication site (DOI): 10.1145/3508230.3508237 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1145/3508230.3508237 (DOI: 10.1145/3508230.3508237)
25.	Reishi Amitani, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Prediction of Number of Likes and Retweets based on the Features of Tweet Text and Images, Proceedings of 2021 5th International Conference on Natural Language Processing and Information Retrieval, Online, Dec. 2021. (Summary) The current study aimed to investigate social media trends and propose an analysis method to explore the factors underpinning the buzz phenomenon on Twitter. As it is not always possible to determine the cause of the buzz phenomenon from the text content alone posted on Twitter, we limited the analysis to tweets with attached images and devised an analysis method using both text and images. We investigated whether there is a relationshipbetween the features of both tweet text and its attached images, and how the relationship between these features is related to the number of likes and retweets (RTs) receivedthat is, indicators of popularity. We trained a multi-task neural network that takes the features extracted from the images and text as input, and then outputs the number of likes and RTs before extracting the feature vectors of the same dimension from the two inputs (images and text, respectively) from the middle layer. By calculating the distance between these feature vectors, we analyzed the relationship between the number of likes and RTs. The results revealed that the average vectors of BERT and inceptionresnetv2 served as predictors of the number of likes and RTs. We also found that tweet text with a low number of likes and RTs was short and simple. (Keyword) Twitter / multi-task learning / neural networks / trend ananlysis (Link to Publication Site) ● Publication site (DOI): 10.1145/3508230.3508244 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1145/3508230.3508244 (DOI: 10.1145/3508230.3508244)
26.	Ryota Kishima, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Construction of MBTI Personality Estimation Model Considering Emotional Information, Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation (PACLIC35), 262-269, Shanghai, Nov. 2021. (Summary) The widespread use of social networking services (SNSs) such as Twitter and Facebook in recent years have rendered it easy to interact with various people all over the world.Analysis of the user personality and emotion using features such as the text information and image information on SNSs has been researched.This study analyzes the effectiveness of using emotional information as a feature when building a personality estimation model for Twitter users. The extensively used Myers-Briggs type indicator (MBTI) is used for analysis. A method is proposed for estimating the MBTI personality pattern by extracting emotional information from Twitter and adding it to the characteristics of the distributed expressions of sentences and words.Through evaluation experiments, it is determined whether the emotional information is effective in constructing the MBTI personality estimation model. The obtained results demonstrate that the accuracy is improved when emotional information is included as a feature, compared to the case without emotional information. (Keyword) MBTI / personality analysis / emotion / Twitter
27.	Kazuyuki Matsumoto, Matsunaga Takumi, Minoru Yoshida and Kenji Kita : Construction of Emotional Similarity Word Embedding Model for Sentiment Analysis, 20th Mexican International Conference on Artificial Intelligence (MICAI2021), Oct. 2021. (Summary) We propose a method for constructing a dictionary of emotional expressions, which is an indispensable language resource for sentiment analysis in the Japanese. Furthermore, we propose a method for constructing a language model that reproduces emotional similarity between words, which to date has yet not been considered in conventional dictionaries and language models. In the proposed method, we pre-trained sentiment labels for the distributed representations of words. An intermediate feature vector was obtained from the pre-trained model. By learning an additional semantic label on this feature vector, we can construct an emotional semantic language model that embeds both emotion and semantics. To confirm the effectiveness of the proposed method, we conducted a simple experiment to retrieve similar emotional words using the constructed model. The results of this experiment showed that the proposed method can retrieve similar emotional words with higher accuracy than the conventional word-embedding model. (Keyword) affective computing / emotional embedding / DistilBERT / deep neural networks / emotion
28.	Kishima Ryota, Kazuyuki Matsumoto, Hirobayashi Tomoki, Seiji Tsuchiya, Minoru Yoshida and Kenji Kita : The Relationship between Personality Patterns and Harmfulness: Analysis and Prediction based on Sentence Embedding, 3rd Technological Competency as Caring in the Health Sciences 2021, Aug. 2021. (Summary) In this paper, we propose a method for detecting harmful contents in social media. In the Social Networking Service (SNS) the articles and messages posted on these services can be viewed by an unspecified number of unknown people from worldwide. Recently, increasing the number of users who have been forced to close their SNS accounts or have been suffering from mental problems because of blew up SNS triggered by hate speech or bullying remarks in social media. Even if the management of the SNS takes countermeasures, it is difficult to deal with all types of harmful expressions. In this research, we focus on the characteristic that harmful statements need to be judged from the entire sentence, and extract features of harmful expressions using a general-purpose language model. (Keyword) harmful expression / sentence embedding / Internet flaming / MBTI / personality analysis
29.	Kazuyuki Matsumoto, Ryu Mopuaa, Minoru Yoshida and Kenji Kita : Lifestyle Analysis via a Corpus of Disease-Fighting Weblogs, Proceedings of 2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML 2021), 266-272, Chengdu, Jul. 2021. (Summary) In recent years, the population of pre-diabetics in Japan has been increasing year by year. Type 2 diabetes is a type of lifestyle-related disease that can be prevented to a certain extent by correcting lifestyle habits. However, by the time we realize that there is a problem with our lifestyle, it may be too late. Therefore, early detection of risk factors for lifestyle-related diseases is important. In this study, we collected the blogs of lifestyle-related disease fighters, set up multiple keyword categories that are considered to be related to risk factors, and constructed a corpus of disease fighting blogs with labels for each category. The results of the evaluation experiments show that our proposed method can be applied to a wide range of topics. As a result of evaluation experiments, our proposed method achieves categorization of keywords and sentences with higher accuracy than the simple method. (Keyword) diabetic patients / lifestyle-related disease / category classification / machine learning (Link to Publication Site) ● Publication site (DOI): 10.1109/PRML52754.2021.9520697 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/PRML52754.2021.9520697 (DOI: 10.1109/PRML52754.2021.9520697)
30.	Minoru Yoshida, Kohno Shogo, Kazuyuki Matsumoto and Kenji Kita : Visualization of the Artist Relations Using Twitter User Profiles, Frontiers in Artificial Intelligence and Applications (FAIA), Fuzzy Systems and Data Mining VI,, Vol.331, 682-689, Nov. 2020. (Link to Publication Site) ● Publication site (DOI): 10.3233/FAIA200746 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85101594489 (DOI: 10.3233/FAIA200746, Elsevier: Scopus)
31.	Fujisawa Akira, Kazuyuki Matsumoto, Kazuki Ohta, Minoru Yoshida and Kenji Kita : ASCII Art Classification Model by Transfer Learning and Data Augmentation, Frontiers in Artificial Intelligence and Applications (FAIA), Fuzzy Systems and Data Mining VI, Vol.331, 608-618, Nov. 2020. (Summary) In this study, we propose an ASCII art category classification method based on transfer learning and data augmentation. ASCII art is a form of nonverbal expression that visually expresses emotions and intentions. While there are similar expressions such as emoticons and pictograms, most are either represented by a single character or are embedded in the statement as an inline expression. ASCII art is expressed in various styles, including dot art illustration and line art illustration. Basically, ASCII art can represent almost any object, and therefore the category of ASCII art is very diverse. Many existing image classification algorithms use color information; however, since most ASCII art is written in character sets, there is no color information available for categorization. We created an ASCII art category classifier using the grayscale edge image and the ASCII art image transformed from the image as a training image set. We also usedVGG16, ResNet-50, Inception v3, and Xceptions pre-trained networks to fine tune our categorization. As a result of the experiment of fine tuning by VGG16and data augmentation, an accuracy rate of 80% or more was obtained in the human category. (Keyword) Text art / ASCII art / transfer learning / fine tuning / data augmentation (Tokushima University Institutional Repository) ● Metadata: 115914 (Link to Publication Site) ● Publication site (DOI): 10.3233/FAIA200738 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3233/FAIA200738 (Tokushima University Institutional Repository: 115914, DOI: 10.3233/FAIA200738)
32.	Kirihara Taiga, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Keyword Extraction from TV Program Viewers Tweet Based on Neural Embedding Model, Frontiers in Artificial Intelligence and Applications (FAIA), Fuzzy Systems and Data Mining VI, Vol.331, 360-369, Nov. 2020. (Summary) In recent years, young people have not been watching television (TV) as much as they used to. This is mainly because a number of TV programs are very long and/or have limited viewing times. Recently, individuals have been actively posting live-action tweets on Twitter to comment on TV content while watching programs in real time. In this study, we propose a method for extracting key phrases related to the event scenes of TV programs using live tweets, and we propose a scene search system that aims at efficient TV program viewing. The experimental results indicated that the program contents were estimated with an error of approximately 5% to 10% with respect to the program time. In addition, the extracted key phrases were visualized for each event scene category using the t-SNE algorithm. (Keyword) natural language processing / TV Program / key phrase extraction / Twitter analysis (Link to Publication Site) ● Publication site (DOI): 10.3233/FAIA200714 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85101584659 (DOI: 10.3233/FAIA200714, Elsevier: Scopus)
33.	Yuki Obayashi, Feni Betriana, Tetsuya Tanioka, Tomoya Yokotani, Ryuichi Tanioka, Chihiro Kawai, Hirokazu Ito, Yuko Yasuhara, Kyoko Osaka, Kazuyuki Matsumoto, Fuji Ren and Yoshihiro Kai : Development issues on communication robots' application in order to use for the older people care, The 15th NLPKE (NLPKE 2020), WEB, Oct. 2020.
34.	Yokotani Tomoya, Tanioka Ryuichi, Kawai Chihiro, Betriana Feni, Hirokazu Ito, Yuko Yasuhara, Kazuyuki Matsumoto, Fuji Ren and Tetsuya Tanioka : Human operator's "Characteristic of autonomic nerve activity as psychological burden" and " Clarification of thinking process for AI development" while operating the humanoid robot conversation program, The 15th NLPKE (NLPKE 2020), WEB, Oct. 2020.
35.	Hirokazu Ito, Kazuyuki Matsumoto, XIN KANG, Tetsuya Tanioka, Yuko Yasuhara, Rozzano De Castro Locsin and Fuji Ren : Examination to develop the artificial intelligence through transforming tacit knowledge of nurses' dialogue for patients with dementia towards explicit knowledge, The 15th NLPKE (NLPKE 2020), WEB, Oct. 2020.
36.	Ryuichi Tanioka, Kazuyuki Matsumoto, Kenichi Sugawara, Kensaku Takase, Yoshihiro Kai, Masahito Tomotake and Tetsuya Tanioka : Issues in developing natural language processing applications for healthcare robots to motivate older people while rehabilitation, The 15th NLPKE (NLPKE 2020), Oct. 2020.
37.	Kohei Torii, Minoru Yoshida, Kazuyuki Matsumoto, Jiro Tsuruki, Kobayashi Kenichiro, Eiichi Honda and Kenji Kita : Towards Automatic Dental Diagnosis System Based on Deep Learning, Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering, 64-65, Online, Oct. 2020.
38.	Ryu Mopuaa, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Construction of Annotated TOBYO Blog Corpus for Lifestyle Disease Analysis of Diabetic Patient, Proceedings of The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE20), 77, Oct. 2020. (Summary) In this study, we target to analyze weblogs authored by diabetes patients describing their struggle against their disease. In order to build a corpus for analysis, we annotate tags to the important keywords/key phrases such as blood glucose level, diet and emotions in collected weblog data. We aim at constructing a labeled lifestyle disease weblog corpus (called as TOBYO weblog corpus) to extract useful information from patients' weblogs of against illness. For example, to improve our lifestyle, it would be very useful to know how we should live every day, what kind of diet we should eat, how much exercise is appropriate, and so on. In our study, we organize the tagged data and take statistics on part-of-speech combinations. We set the top N thresholds in the rank of the frequency of the part-of-speech and create a system to exclusively pre-process the key phrases that are ranked as more frequent part of speech combinations. The extracted key phrase information is vectorized by a model of BERT (bidirectional encoder representation from a transformer), then the key phrases are sorted out by making a model for category prediction that uses two inputs (BERT vector and character frequency vector). To evaluate our proposed method, we conduct cross validation test and open test by using several machine learning algorithms. In comparison with the baseline classification method, which is a simple and ordinal neural network method, our method can achieve higher performance. (Keyword) Tobyo corpus / neural networks / weblog / lifestyle-related disease / diabetes
39.	Liu Maobo, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Construction of lifestyle disease weblog corpus and evaluation of keyword classification model, International Forum on Advanced Technologies 2020 (IFAT2020), Mar. 2020. (Keyword) lifestyle-related disease / weblog corpus / keyword classification
40.	Kazuyuki Matsumoto, Manabu Sasayama, Minoru Yoshida and Kenji Kita : Emotional State Estimation by Dialogue History and Sentence Distributed Representation, Proceedings of 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems(CCIS2019), 31-35, Singapore, Dec. 2019. (Summary) The paper presents a novel approach to predict humans emotion in the dialogue. We target the scenario dialogue corpora. The corpora are annotated by some subjects with emotion tag. We use sentence embedding by Bidirectional Encoder Representations from Transformers as versatile and rich feature. By acquiring multi-context information from dialogue text, the accuracy of emotion prediction can be improved. As an experiment by machine learning algorithm such asdeep neural networks for the proposed method, we achieved higher accuracy than the method using embedding feature from utterance itself. (Keyword) dialogue sentences / dialogue emotion estimation / sensibility robotics (Link to Publication Site) ● Publication site (DOI): 10.1109/CCIS48116.2019.9073750 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85085005205 (DOI: 10.1109/CCIS48116.2019.9073750, Elsevier: Scopus)
41.	Yuko Sakuta, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Kansei Mapping of Color Images Using Earth Mover's Distance, International Conference on Software Technology and Engineering (ICSTE2019), Oct. 2019.
42.	Akira Fujisawa, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : An Approach for Conversion of Japanese Emoticons into Emoji Based on Character-Level Neural Autoencoder, Frontiers in Artificial Intelligence and Applications, Vol.320, 635-644, Oct. 2019. (Summary) In this paper, we propose a method for converting Japanese emoticons into emoji. The method creates a model that translates text into emoji using training neural networks with a character-based feature; the model determines the positive/negative/neutral polarity of the emoji from the text. By extracting a feature vector from the hidden layer of the model, we calculate the similarity between the input sentence and the text annotated with emoji in the database; the conversion candidates come from the emoticon in the input text. In comparison tests of the proposed method and a word-level feature method that uses a word distributed representation vector, the emoji emotion polarity-based model achieved a maximum accuracy rate of 90.0%, representing an 8% improvement over the word-level feature method. (Keyword) emoji / emoticon / neural autoencoder (Link to Publication Site) ● Publication site (DOI): 10.3233/FAIA190231 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3233/FAIA190231 (DOI: 10.3233/FAIA190231)
43.	Kazuyuki Matsumoto, Mopaa Ryu, Minoru Yoshida and Kenji Kita : Emotion Analysis on Weblog of Lifestyle Diseases, Proceedings of the 2019 International Symposium on Signal Processing Systems (SSPS2019), 142-148, Beijing, Sep. 2019. (Summary) In this paper, we investigate the relation between patient lifestyle and emotional state transition by analyzing weblog articles written by patients with diabetes. To conduct our analysis, we use blood sugar level and body weight to indicate the disease condition, together with meal keywords and sentences that express feelings in the selected weblogs. A corpus annotated with a tag to word units in the target articles is constructed and a method of keyword classification based on the constructed corpus is proposed and evaluated. Finally, we analyze whether the indicated emotion is positive or negative in sentences expressing emotion in the corpus and investigate the relation between the emotion and a change in blood sugar level. (Keyword) text mining / diabetes / lifestyle diseases / emotion analysis / machine learning (Link to Publication Site) ● Publication site (DOI): 10.1145/3364908.3365294 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85077207726 (DOI: 10.1145/3364908.3365294, Elsevier: Scopus)
44.	Kazuyuki Matsumoto, Yuta Hada, Minoru Yoshida and Kenji Kita : Analysis of Reply-Tweets for Buzz Tweet Detection, Proceedings of 33rd Pacific Asia Conference on Language, Information and Computation (PACLIC33), 138-146, Sep. 2019. (Summary) In this study, we propose a method for predicting whether a tweet will create a buzz on the Internet by examining tweeted replies posted by others. We also investigate the distinguishing characteristics of replies to buzz tweets by analyzing feature amounts. Our proposed method first converts each reply tweet into a vector expression using a word distributed representation or some other vectorization method. We then apply a machine learning method for binary classification todetermine whether the reply is to a buzz tweet or a non-buzz tweet. We classify the target tweet into buzz or non-buzz categories by comparing the total buzz and non-buzz scores produced by the classifier. The proposed method using StarSpace achieved 93.1% F1-score, while an approach that used number of retweets and number of favors (likes) achieved 77.8% F1-score. Wealso found that there are a number of words that are characteristic of buzz tweet replies anda number of words that are characteristic of non-buzz tweet replies. (Keyword) text mining / Twitter / buzz tweet / StarSpace / Deep learning (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85084953625 (Elsevier: Scopus)
45.	Kazuyuki Matsumoto, Manabu Sasayama, Minoru Yoshida and Kenji Kita : Personality Analysis on Dialogue System for Emotional Interaction Robot, Conference Proceedings of Asia Pacific Society for Computing and Information Technology 2019 Annual Meeting (APSCIT 2019 Annual Meeting), Jul. 2019. (Summary) In this study, we proposed the personality analysis method for dialogue system towards realization of emotional interaction robot. To achieve smooth communication between human and robot in dialogue, it is necessary that overcome feeling of strangeness by generating dialogue utterance based on consistent personality. Firstly, we extract the wording feature from utterance texts on social media. To obtain relation between the wording feature and personality ego-state level, we use sentence level embedding feature and create state level estimator for each ego-state. Next, we evaluate the ego-state level estimator with an evaluation test set. Finally, we use this estimator for analysis of chat-bot dialogue. By comparing the existing chat-bot dialogue and user utterance, we consider about non-reality of current chat-bot dialogue generation. (Keyword) personality analysis / dialogue system / egogram / emotional interaction robot
46.	Kojima Takumi, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Research on statements in tweets and sleep time of classification, the 3rd International Conference on Natural Language Processing and Information Retrieval, Tokushima, Japan, Jun. 2019.
47.	Hidetoshi Nakao, Masakazu Imaoka, Kenji Oka, Mitsumasa Hida, Takeshi Morifuji, Masashi Hashimoto, Kazuyuki Matsumoto and Kenji Kita : Factor Affecting Low-Arch and High-Arch of Foot, 13th International Society of Physical and Rehabilitation Medicine (ISPRM 2019), No.P3-447, Jun. 2019.
48.	Hidetoshi Nakao, Masakazu Imaoka, Kenji Oka, Mitsumasa Hida, Takeshi Morifuji, Masashi Hashimoto, Kazuyuki Matsumoto and Kenji Kita : Body Factors of Low Arch of Foot for Middle and Senior Age, World Confederation for Physical Therapy (WCPT2019), No.PO-E-30-SUN2, May 2019.
49.	Horikawa Haruki, Minoru Yoshida, Kenji Kita and Kazuyuki Matsumoto : Extended Dataset for Food Image Classification Based on Twitter, Hong Kong International Conference on Engineering and Applied Science (HKICEAS 2018), Dec. 2018.
50.	Kataoka Asaki, Sasayama Manabu and Kazuyuki Matsumoto : Data Augmentation for Lyric Emotion Estimation, 2018 International Conference on Creativity, Innovation, and Invention in Digital Technology (CIIDT2018), Dec. 2018. (Summary) 歌詞感情推定が実現すれば，テキスト検索やメロディ検索だけでなく，歌詞に含まれる感情や楽曲全体の感情の流れに基づいた楽曲検索システムおよび楽曲推薦システムが実現できる．そのためには，フレーズごとの歌詞感情コーパスが必要となる．しかし，フレーズの感情は人手で付与されるため，大規模な歌詞感情コーパスを作成するのは難しい．本稿では，歌詞感情コーパスの拡張手法を提案する．拡張前後の歌詞感情コーパスでそれぞれ学習した畳み込みニューラルネットワークを用い，提案手法の有効性を検証する．推定する歌詞の感情はJoy，Love，Anger，Sorrow，Anxietyの5クラスとする． (Keyword) lyric / emotion estimation / data augmentation
51.	Kazuyuki Matsumoto and Manabu Sasayama : Lyric Emotion Estimation Using Word Embedding Learned from Lyric Corpus, Proceedings of the 4th IEEE International Conference on Computer and Communications, Vol.VI, 2295-2301, Chengdu, Dec. 2018. (Summary) In searching huge music databases, users typically use title, artist or song writer's name as their search query. When users have only vague search conditions, searching for a song using affective information - that is, emotional impressions of the piece such as relief or sorrow - can be an appealing possibility. For cases in which the song's audio information consists of vocal and instrumental performances, both words and music reflect the writer's thoughts and emotions and encourage the imagination of the listeners. While a number of studies have used audio and lyric features in the emotion estimation of songs, many of the studies that use only lyrics have been based on existing textbook emotion estimation methods. Because the way in which words used in song lyrics tends to differ from the way they are used in text sentences, an emotion estimation method specific to song lyrics would be more appropriate. In this study, we extract features from a lyric corpus to handle lyric-specific expressions and propose a method to estimate emotion from each lyric phrase. The results of our evaluation experiment confirmed the effectiveness of the proposed method (lyric corpus word vector + 3-NN), as it produced a higher F-measure than the other methods. (Keyword) lyric / emotion estimation / word embedding / k-nearest neighbor / support vector regression (Link to Publication Site) ● Publication site (DOI): 10.1109/CompComm.2018.8780811 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/CompComm.2018.8780811 (DOI: 10.1109/CompComm.2018.8780811)
52.	Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Modeling Relations Between Profiles and Texts, Proceedings of the 14th the Asia Information Retrieval Societies Conference (AIRS 2018), LNCS 11292, 103-109, Taipei, Nov. 2018. (Summary) We propose a method to model Twitter texts and user profiles simultaneously by considering the relations between the texts and profiles to obtain the distributed representations of the words in both. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-030-03520-4_10 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85057273674 (DOI: 10.1007/978-3-030-03520-4_10, Elsevier: Scopus)
53.	Akira Fujisawa, Kazuyuki Matsumoto, Ohta Kazuki, Minoru Yoshida and Kenji Kita : ASCII Art Category Classification based on Deep Convolutional Neural Networks, Proceedings the 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), 345-349, Nanjing, Nov. 2018. (Summary) In text-based communications on social media, emoticons, emojis, and stamps provide important ways to convey emotion and facilitate smoother communication in a short text. As non-verbal expressions, the characters in ASCII Art have no inherent meaning. However, by using a characters position or shape, emotion or intention can be expressed visually. Until now, it has been difficult to classify ASCII Art using the ASCII Art characters. In this paper, we propose a method for classifying ASCII Art by converting ASCII Art into images and using deep learning. In an evaluation experiment, the proposed method was able to achieve a classification success rate as high as 97%. (Keyword) ASCII Art / classification / Deep learning / Convolutional Neural Networks (Link to Publication Site) ● Publication site (DOI): 10.1109/CCIS.2018.8691245 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85064980913 (DOI: 10.1109/CCIS.2018.8691245, Elsevier: Scopus)
54.	Kazuyuki Matsumoto, Manabu Sasayama, Minoru Yoshida, Kenji Kita and Fuji Ren : Transfer Learning Based on Utterance Emotion Corpus for Lyric Emotion Estimation, Proceedings of 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS), 699-703, Nanjing, Nov. 2018. (Summary) In prior research, there are various emotion estimation approaches that are corpus-based, dictionary-based, or rule-based. However, it is necessary to prepare language resources suitable to each domain, which will incur substantial cost. In this paper, we propose an approach to estimate emotion of lyric phrase by using transfer learning. We applied the existing utterance emotion corpus to the lyric emotion estimation task. As the result of an evaluation experiment, we obtained higher accuracy than the baseline method using a simple bag of words feature tf-idf weighted vector and logistic regression model. (Keyword) Transfer learning / Emotion estimation / Lyric / Word embedding (Link to Publication Site) ● Publication site (DOI): 10.1109/CCIS.2018.8691207 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85064985706 (DOI: 10.1109/CCIS.2018.8691207, Elsevier: Scopus)
55.	Bando Koji, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Twitter User's Hobby Estimation Based on Sequential Statements Using Deep Neural Networks, 2018 10th International Conference on Software Technology and Engineering, Oct. 2018. (Keyword) Twitter / hobby estimation / Neural Networks / random forests / support vector machine
56.	Yunong Wu, XIN KANG, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Emoticon-based emotion analysis for Weibo articles in sentence level, Lecture Notes in Computer Science, Vol.11248, 104-112, Oct. 2018. (Keyword) Emotion Analysis / Emoticon (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-030-03014-8_9 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85057082718 (DOI: 10.1007/978-3-030-03014-8_9, Elsevier: Scopus)
57.	Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Classification of Emoji Categories from Tweet Based on Deep Neural Networks, Proceedings of the 2nd International Conference on Natural Language Processing and Information Retrieval (NLPIR2018), 17-25, Bangkok, Sep. 2018. (Summary) In this paper, we describe the sentiment analysis method from tweets based on emojis category. Many of existing study about sentiment analysis focused on the emotional expressions included in sentence. However, because there are various kinds of emotional expressions, such as Internet slang, it cannot be constructed that the fixed emotional expression dictionary. The most of the methods based on corpus and machine learning, its performance is quite depended on the quality of annotation. Therefore, we attempt to use categories which are expressed by emoji as sentiment label instead of manually annotated labels. Our proposed method uses automatically annotated category label by emoji which is annotated to sentence, and train word embedding feature by deep neural networks. As the result of the experiment, our proposed method overcome the simple word feature based method. (Keyword) Emoji / Twitter / deep neural networks (Link to Publication Site) ● Publication site (DOI): 10.1145/3278293.3278306 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1145/3278293.3278306 (DOI: 10.1145/3278293.3278306)
58.	Nakao Hidetoshi, Jirela Dilixiati, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Emotion Recognition from Emoticons using Convolutional Neural Networks, Proceedings of 2nd Technological Competency as Caring in the Health Sciences 2018, 72, Aug. 2018. (Summary) In recent years, text communication has been grown steadily so that all kinds of daily contacts could be conducted now through Internet based text messaging services. As an advantage of text-based communication via the Internet, we can quickly interact with anyone wherever we are. At the same time, since it is difficult to express emotions with each other only with text, the chance of adding non-verbal information such as photos to text is increasing. Among the non-verbal information, there are emoticons in expressions that have been developed by Internet bulletin boards, chat, e-mail, etc. In particular, the types of emoticons used in Japan are diverse, even now; various types of emoticons continue to increase. There have been studies on these emoticons until now, which have become increasingly important in recent years. In this paper, we propose a method to classify facial expressions expressed from emoticons by letting deep convolutional neural networks. When recognizing emotions from emoticons, it is not known that seeing emoticons in character units is very effective. Information such as role positioning and coexistence roles is necessary. Characters in emoticons differ depending on font it also gives different impressions depending on context. In order to realize semantic analysis / emotion analysis considering emoticons, it is necessary to provide a corpus that records various kinds of emoticons and a database that gives information such as meanings and emotions expressed by emoticons. However, including all the emoticons is difficult, and since the emoticons are more vague than ordinary language expressions, that is why the construction of the dictionary is delayed. In this paper, we propose a method to classify the emotions of the emoticons by letting the deep convolutional neural network learn the features of the emoticons. By treating the emoticon as an image, the visual features can be captured and it is considered that natural recognition can be performed instead of processing like a character string. In addition, since the emoticon characterizes the facial expression with a character string, there is a problem that the impression is different only when the used fonts are different from each other, and therefore, when different fonts are used, how the classification accuracy is changed, we consider whether to do so. (Keyword) Emoticon / Convolutional Neural Networks / Emotion recognition / Data augmentation
59.	Yunong Wu, XIN KANG, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Sentence Emotion Classification for Intelligent Robotics Based on Word Lexicon and Emoticon Emotions, Proceedings of the International Conference of Intelligent Robotic and Control Engineering, 38-41, Aug. 2018. (Keyword) robotics / natural language processing (Link to Publication Site) ● Publication site (DOI): 10.1109/IRCE.2018.8492969 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85056756985 (DOI: 10.1109/IRCE.2018.8492969, Elsevier: Scopus)
60.	Ryusei Matsumoto, Minoru Yoshida, Kazuyuki Matsumoto, Hironobu Matsuda and Kenji Kita : Visualization of the occurrence trend of infectious diseases using Twitter, Proceedings of the 11th edition of the Language Resources and Evaluation Conference (LREC 2018), (poster), 511-514, May 2018. (Summary) We propose a system for visualizing the epidemics of infectious diseases. We apply factuality analysis both to disease detection and location estimation for accurate visualization. We tested our methods for several infectious diseases, and show that our method performs well on various diseases. (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85059887520 (Elsevier: Scopus)
61.	Jirela Dilixiati, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Predicting facial expression from emoticon based on convolutional neural networks, 4th International Forum on Advanced Technologies (IFAT 2018), Mar. 2018. (Summary) Text communication has been developed in recent years, all kinds of daily contacts are through the SNS regardless of age, gender and active. As an advantage of text-based communication via the Internet. There is a point that you can quickly respond to anyone wherever you are. In this paper, by letting the deep convolution neural network learn the image features of emoticons. We propose a method to classify by emotion expressed by emoticons. By treating emoticons as images, it is possible to capture visual features and think that natural identification can be made rather than handling as character strings. (Keyword) emoticon / convolutional neural network / emotion / facial expression
62.	Kenji Kita, Yunong Wu, Kazuyuki Matsumoto and Minoru Yoshida : The Effectiveness of Image Data Augmentation for Deep Learning, Conference Abstract of the 3rd International Conference on Multimedia and Image Processing, 19, Mar. 2018. (Keyword) Deep Learning / Data Augmentation / image processing
63.	Akira Fujisawa, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Visual Similarity for ASCII Arts Using Image Features, Proceedings of the 2018 2nd International Conference on Machine Vision and Information Technology (CMVIT 2018), 17, Hong Kong, Feb. 2018. (Summary) Communication with various types of non-verbal expressions has been increasing on Social Networking Services. Among them, ASCII art is a character-based design technique to express human emotions. In many cases, ASCII arts are generated from original images by mapping some region of pixels to a certain character. In the field of natural language processing, ASCII arts are treated as sequences of characters. In addition, there have been few studies that aim to analyze or classify ASCII arts. In this paper, we extract characteristics of ASCII arts using image shape features. Our image shape-based features provide visual similarity between ASCII arts that are invariant across image scale and characters used in ASCII arts. Moreover, the proposed method enable us to compare visual similarity between ASCII arts and original images. To extract shape features from ASCII arts, we use image features of original images by converting ASCII arts to images. In the experiment, we compared visual similarity between ASCII arts and original images by using Histograms of Oriented Gradients(HOG). Our experimental result shows that the proposed method is effective to compare visual similarity between ASCII arts and original images. (Keyword) visual similarity / ascii art / image feature
64.	Yunong Wu, Kenji Kita, Kazuyuki Matsumoto, XIN KANG and Fuji Ren : Exploring the Relations Between Posts and Comments for Short Text Conversation, Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE17), 228-238, Chengdu, Dec. 2017.
65.	Kazuyuki Matsumoto, Fuji Ren, Minoru Yoshida and Kenji Kita : Review Score Estimation Based on Transfer Learning of Different Media Review Data, Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering(NLP-KE'17), 185-199, Dec. 2017. (Summary) In this paper, we proposed a model to classify reviews based on review data of different media. Recently, researches have been actively made on transfer learning between different domains with various kinds of big data as a target. Evaluation expressions are usually different in different domains, and this becomes a barrier for reputation analysis. Users usually use different linguistic expressions to refer to the creative works of each media. For example, media such as "anime", "comics", "games" and "movies" have different terms or expressions to be described their creative works. These differences can be considered showing features of each medium and such differences should be found in other expressions as well as in evaluation expressions. We analyzed what effect such differences would cause to classication accuracy by conducting transfer learning between review data ofdifferent media. In this paper, we proved compatibility between original (pre-transferring) and target (post-transferring) media for each medium of the creative works by constructing a review classication model. As a result of evaluation experiments, we could more accurately estimate review score without using SO-Score for training of review fragment based on Long Short Term Memory (LSTM) than the SO-Score based method. (Keyword) review classification / transfer learning / Long Short-Term Memory / different media
66.	Hirokimi Fukuda, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Index Generation of BGM Video Based on Distinctive Comments, Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering(NLP-KE'17), 179-184, Dec. 2017. (Summary) The purpose of this study is to generate indexes to the BGMs based on the distinctive comments annotated to the BGM videos posted on Nico Nico Videos. Our proposed method detects the end/start positions of the BGMs considering the increase/decrease of the number of the comment data, the comments distinctive to the songs and the exclamatory expressions annotated to the videos. The evaluation result showed that the proposed method could generate correct indexes within 10 seconds error for the BGM videos with length of less than 40 minutes and with annotation of over 30,000 comments. (Keyword) Nico Nico Video / index generation / BGM video for work
67.	Naoya Fujino, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Emotion Estimation Adapted to Gender of User Based on Deep Neural Networks, Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering(NLP-KE'17), 166-178, Dec. 2017. (Summary) In this study, we focus on a representative SNS, Twitter and target emotion estimation from tweets posted on Twitter. we construct emotion estimation models applicable to each gender. It is considered that there would be different word usage tendencies depending on gender. Therefore, we analyze what difference exists in usage tendency of slangs or emoji, which are thought to be related to emotion, depending on gender. Then, we propose a method to improve emotion estimation based on neural networks by usingdistributed representation models for each gender. As the result of the evaluation experiments, the higher estimation accuracy was realized by training with Deep Convolutional Neural Networks using word's distributed representation as feature than by training with Feed Forward Neural Networks. (Keyword) emotion estimation / user's gender / deep neural networks
68.	Akira Fujisawa, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Facial Expression Classification Based on Shape Feature of Emoticons, Proceedings of 1st International Conference on Machine Learning and Data Engineering (iCMLDE2017), 29-34, Sydney, Nov. 2017. (Summary) Emoticons are used in the situation of textual communication such as web mails and internet forums. Many of the existing studies dealing with classification or extraction of emoticons regard emoticons as a kind of character string and focus on what characters constitute the emoticons or how they are lined up. However, emoticons are used to express human facial expressions, and characters constituting them represent various facial parts such as eyes, nose, mouth, etc. Such characters can be identified as different facial parts depending on their positions, and facial expressions are thought to be represented by the combinations of their shape features. In this study, we classified the facial expressions of emoticons by focusing on the shape features of those emoticons. To deal with shape features of emoticons, we converted emoticons, which are text data, to image data. Emoticons are mainly formed by line segments of characters, and use only black and white colors. Therefore, other factors such as colors and shades were not considered as the feature to classify the facial expressions. In the experiments, we used image features that did not require color information. As the result of comparative experiment with the 1-nearest neighbor method using character features, the facial expression recognition rate is 52% when using the Histograms of Oriented Gradients(HOG) was used as image feature. By this result, proposed method improved recognition rate by 2 % than using baseline. (Keyword) facial expression classification / emoticon / image feature
69.	Takeshi Kusumoto, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Gender Estimation from Facial Images by Deep Neural Network, Proceedings of Annual Conference on Engineering and Applied Science, Nov. 2017. (Summary) In this paper, we collect new learning data and learned this by the existing neural network in order to exceed the accuracy of the model of the existing research. As a result, we succeeded in improving the accuracy by increasing the learning data. However, we discovered that performance declined due to biased learning data. The future task is to further improve accuracy and Research the relationship between bias and accuracy of learning data. (Keyword) gender estimation / facial image / deep neural network / image processing / deep learning
70.	Kazuyuki Matsumoto, Akira Fujisawa, Minoru Yoshida and Kenji Kita : Emotion Recognition of Emoticon Based on Character Embedding, 9th International Conference on Software Technology and Engineering (ICSTE2017), Hong Kong, Oct. 2017. (Summary) This paper proposes a method for emotion estimation of emoticon based on distributed representations of character meanings of the emoticon. The existing studies on emoticon have tried to extract emoticons from texts, estimate emotions of emoticons by breaking them up into constituent parts and by using their combination as feature. Based on the recently developed technique for word embedding, we try to realize versatile emotion estimation from emoticons by training the meanings of the characters constituting the emoticons and by using them as feature unit of emoticon based on the recent developed word embedding technique. The cross validation test was conducted for the emoticons emotion estimation model that was constructed based on the deep convolutional neural networks using distributed representations of characters as feature, and its result showed that our proposed method can estimate emotion of unknown emoticons with higher F1-score than the method based on the character n-gram appearance frequency vector as feature. (Keyword) emoticon / emotion recognition / character embedding / convolutional neural networks
71.	Kenji Kita, Kazuyuki Matsumoto and Minoru Yoshida : Face Identification Based on Deep Convolutional Neural Networks, 9th International Conference on Graphic and Image Processing, Oct. 2017. (Keyword) Face Identification / Deep Neural Networks
72.	Kazuyuki Matsumoto, Miyake Takeshi, Seiji Tsuchiya, Minoru Yoshida and Kenji Kita : Flame Prediction Based on Harmful Expression Judgement Using Distributed Representation, Book of Abstracts Proceeding of Engineering & Technology, Computer, Basic and Applied Sciences (ECBA-2017), Vol.3, No.16, 11, Taipei, Sep. 2017. (Summary) In recent years, flaming on social media has been a problem. To avoid flaming, it is useful for the system to automatically check the sentences whether they include the expressions that are likely to trigger flaming or not before posting messages. In this research, we target two harmful expressions. There are insulting expressions and the expressions that are likely to cause a quarrel. Firstly, we constructed an original harmful expression dictionary. Because a large cost requires to collect the expressions manually, we constructed the dictionary semi-automatically by using word distributed representations. The proposed method used distributed representations of the harmful expressions and general expressions as features, and constructed a classifier of harmful/general based on those features. An evaluation experiment found that the proposed method could extract harmful expressions with accuracy of approx. 70%.On the other hand, it was found that the proposed method could also extract unknown expressions, however, it wrongly extracted non-harmful. (Keyword) Flame Prediction / Harmful Expression / Distributed Representation / Support Vector Machine
73.	Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Distributed Representations for Words on Tables, Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017), Part-I (LNAI 10234), 135-146, May 2017. (Summary) We consider a problem of word embedding for tables, and we obtain distributed representations for words found in tables. We propose a table word-embedding method, which considers both horizontal and vertical relations between cells to estimate appropriate word embedding for words in tables. We propose objective functions that make use of horizontal and vertical relations, both individually and jointly. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-319-57454-7_11 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85018412327 (DOI: 10.1007/978-3-319-57454-7_11, Elsevier: Scopus)
74.	Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Acceleration of Similar Word Search in Distributed Representation, Proceedings of the 18th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING 2017) (poster), Apr. 2017. (Summary) We report our study on accelerating search for similar words in distributed representation using cosine similarity measure.Our main idea is to convert similarity value to Euclidean distance, and apply the branch-and-bound approach to distance calculation itself, which means to terminate distance calculation when the current value exceeds the current N-th best similarity value when N is the size of final list of words similar to the query.We also apply other techniques for speeding up such as converting vector representation using PCA and reduction of adding and multiplying operations in distance calculation.
75.	Kenji Kita, Kazuyuki Matsumoto, Minoru Yoshida and Yunong Wu : A Comparison of Face Recognition Features Derived from Convolutional Neural Networks, Conference Abstracts of the 2017 International Conference on Frontiers of Image Processing, 17, Kathmandu, Nepal, Mar. 2017.
76.	Kazuyuki Matsumoto, Tanaka Satoshi, Minoru Yoshida, Kenji Kita and Fuji Ren : Ego-state Estimation from Short Texts Based on Sentence Distributed Representation, The 11th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE'16), Dec. 2016. (Keyword) ego-state / egogram / deep neural network / sentence distributed representation
77.	Akira Fujisawa, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Improvement of the Histogram for Infrequent Color-Based Illustration Image Classification, Fuzzy Systems and Data Mining II, Frontiers in Artificial Intelligence and Applications, Vol.293, 299-305, Dec. 2016. (Keyword) Illustration image / Color histogram / Image classification / Style (Link to Publication Site) ● Publication site (DOI): 10.3233/978-1-61499-722-1-299 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3233/978-1-61499-722-1-299 (DOI: 10.3233/978-1-61499-722-1-299)
78.	Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Table Topic Models for Hidden Unit Estimation, Proceedings of the 12th Asia Information Retrieval Societies Conference (AIRS2016), LNCS 9994, 302-307, Dec. 2016. (Summary) We propose a method to estimate hidden units of numbers written in tables. We focus on Wikipedia tables and propose an algorithm to estimate which units are appropriate for a given cell that has a number but no unit words. We try to estimate such hidden units using surrounding contexts such as a cell in the first row. To improve the performance, we propose the table topic model that can model tables and surrounding sentences simultaneously. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-319-48051-0_23 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-85007107751 (DOI: 10.1007/978-3-319-48051-0_23, Elsevier: Scopus)
79.	Kazuyuki Matsumoto, Minoru Yoshida, Kenji Kita, Yunong Wu and Iwasa Fumihiro : Effect of Users Attribute on Emotion Estimation from Twitter, Proceedings of the 2nd IEEE International Conference on Computer and Communications, Vol.3, 1186-1190, Oct. 2016. (Summary) This study focused on emotion estimation fromutterances on Twitter and analyzed the differences caused forestimation according to the attributes of users. SNS userspossess various user attributes. If we could clarify the tendencyfor each attribute to involve emotion estimation, we would beable to create an emotion estimation model suitable for eachattribute. This study considered two attributes, sex and job, andinvestigated the tendency of emotion estimation based on anemotion estimation model constructed by machine learning. Inthis paper, as a feature for machine learning, we used asentence vector that was obtained by the summation of theword distributed expressions. As a result of the evaluationexperiment using a method based on the nearest neighbormethod, we obtained 0.4 higher estimation accuracies thanusing a baseline method based on a simple Bag of Words model. (Keyword) user attribute / emotion recognition / Twitter (Link to Publication Site) ● Publication site (DOI): 10.1109/CompComm.2016.7924892 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/CompComm.2016.7924892 (DOI: 10.1109/CompComm.2016.7924892)
80.	Kazuyuki Matsumoto, Seiji Tsuchiya, Minoru Yoshida and Kenji Kita : Judgment of Slang Based on Character Feature and Feature Expression Based on Slangs Context Feature, Communications in Computer and Information Science, Vol.652, 280-288, Kuala Lumpur, Sep. 2016. (Summary) This paper proposes a method to distinguish standardwords from slang words using information from the characters comprising thecharacter string. An experiment testing the effectiveness of our method showeda 30 % or more improvement in classification accuracy compared to the baselinemethod. We also use a contextual feature related to emotion to expand theunregistered slang word in the training data into other expressions and proposean emotion estimation method based on the expanded expressions. In our experiment,successful emotion estimation was obtained in nearly 54 % of the cases, anotably higher rate than with the baseline method. Our proposed method wasshown to have validity. (Keyword) Slang / Character feature / Context feature / Unknown expression (Link to Publication Site) ● Publication site (DOI): 10.1007/978-981-10-2777-2_25 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84989337887 (DOI: 10.1007/978-981-10-2777-2_25, Elsevier: Scopus)
81.	Zhang Guoliang, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Face Identification based on Deep Learning Using Low Pixel Images, International Conference on Engineering & Technology, Computer, Basic & Applied Sciences, Jul. 2016.
82.	Kazuyuki Matsumoto, Manabu Sasayama, Kohsuke Miyauchi, Minoru Yoshida and Kenji Kita : Lyric Retrieval System Based on Word's Intimacy Value, Advances in Engineering Research, Vol.24, 84-87, May 2016. (Keyword) Lyric retrieval / Query expansion / Word intimacy value
83.	Kazuyuki Matsumoto, Minoru Yoshida, Kenji Kita and Fuji Ren : An Approach to Refine Translation Candidates for Emotion Estimation in Japanese-English Language, Proceedings of the 7th International Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management, Vol.2, 74-83, Nov. 2015. (Summary) Researches on emotion estimation from text mostly use machine learning method. Because machine learning requires a large amount of example corpora, how to acquire high quality training data has been discussed as one of its major problems. The existing language resources include emotion corpora; however, they are not available if the language is different. Constructing bilingual corpus manually is also financially difficult. Wepropose a method to convert a training data into different language using an existing Japanese-English parallel emotion corpus. With a bilingual dictionary, the translation candidates are extracted against every word of each sentence included in the corpus. Then the extracted translation candidates are narrowed down into a set of words that highly contribute to emotion estimation and we used the set of words as training data. As theresult of the evaluation experiment using the training data created by our proposed method, the accuracy of emotion estimation increased up to 66.7% in Naive Bayes. (Keyword) Emotion Corpus / Emotion Estimation / Bilingual Tagged Corpus
84.	Kazuyuki Matsumoto, Minoru Yoshida, Seiji Tsuchiya, Kenji Kita and Fuji Ren : Slang Analysis Based on Variant Information Extraction Focusing on the Time Series Topics, the 10th International Conference on Natural Language Processing and Knowledge Engineering, Sapporo, Japan, Oct. 2015.
85.	Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Sensibility Estimation Method for Youth Slang by Using Sensibility Co-occurrence Feature Vector Obtained from Microblog, Proceedings of the 2015 IEEE International Conference on Computer and Communications (ICCC2015), Oct. 2015. (Summary) Social networking sites such as Twitter provide more opportunities to express what people think or intend in short text. In short text, abbreviations such as ASAP or joinus and emoticons are often used. Because these expressions are not registered into the existing dictionaries, these are analyzed as unknown expressions. That can be a bottleneck for improving accuracy of reputation analysis in text mining. To use context for unknown word clustering is a major method, however, it usually requires word segmentation process and it has weakness for split errors of unknown expressions such as youth slang. In this paper, we proposed a method to obtain the appropriate context even though unknown expressions cause split errors and estimate sensibility expressed in the text. Because the dimensions of the obtained context vector were enormous, we also proposed a method to create a feature vector based on the co-occurrence of the sensibility words as simple expression with low dimension. As an evaluation experiment, the proposed method showed certain accuracy even with the small training data. (Keyword) slang / microblog / Twitter / affective computing / sensibility classification (Link to Publication Site) ● Publication site (DOI): 10.1109/CompComm.2015.7387618 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84963939887 (DOI: 10.1109/CompComm.2015.7387618, Elsevier: Scopus)
86.	Kazuyuki Matsumoto, Kyosuke Akita, Minoru Yoshida, Kenji Kita and Fuji Ren : Estimate the Intimacy of the Characters Based on Their Emotional States for Application to Non-Task Dialogue, Proceedings of the 6th International Conference on Affective Computing and Intelligent Interaction (ACII2015), 327-333, Xi'an, Sep. 2015. (Summary) Non-task-oriented dialogue requires someschemes that enable smooth and flexible conversations with auser. For example, it would be possible to go beyond the closedrelationship between the system and the user by consideringthe users relationship with others in real life. In this paper,we focused on the dialogue made by the two characters in adrama scenario, and tried to express their relationship with ascale of intimacy degree. There will be such various elementsrelated to the intimacy degree as the frequency of response to theutterance and the attitude of a speaker during the dialogue. Wefocused on the emotional state of the speaker during the utteranceand tried to realize intimacy estimation with higher accuracy. Asthe evaluation result, we achieved higher accuracy in intimacyestimation than the existing method based on speech role. (Keyword) Intimacy Estimation / Scenario Corpus / Emotion Recognition (Link to Publication Site) ● Publication site (DOI): 10.1109/ACII.2015.7344591 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84964027376 (DOI: 10.1109/ACII.2015.7344591, Elsevier: Scopus)
87.	Kazuyuki Matsumoto, Minoru Yoshida, Qingmei Xiao, Luo Xin and Kenji Kita : Emotion Recognition for Sentences with Unknown Expressions based on Semantic Similarity by Using Bag of Concepts, Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'15), 1428-1433, Aug. 2015. (Summary) In studies of emotion estimation from text, varietiesof methods have been attempted such as emotional expressiondictionary or sentence structure dictionary and emotion corpus.However, most of these methods targeted the expressions includedin the existing morphological analysis dictionaries, as a result,they did not pay enough attention to unknown words, especiallynewly coined words. In Japan, the growth of Internet communicationsites such as weblogs and social networking sites broughtyounger people especially in teens and in their 20s to create newwords and use them very often. We prepared an emotion corpusby collecting weblog article texts including new words, analyzedthe corpus statistically, and proposed a method to estimateemotions of the texts. Most slang such as Youth Slang is tooambiguous in sense classification to be registered into the existingdictionaries such as thesaurus. To cope with these words, wecreated a large scale of Twitter corpus to calculate sense similaritybetween words. We proposed to convert unknown to sense classid to process the words that were not included in learning data.We defined this as a method using Bag of Concepts as feature. Asa result of the evaluation experiment using several classifies, theproposed method was proved robustness for unknown expression. (Keyword) Bag of concepts / emotion recognition / unknown expression / semantic similarity (Link to Publication Site) ● Publication site (DOI): 10.1109/FSKD.2015.7382148 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84966470618 (DOI: 10.1109/FSKD.2015.7382148, Elsevier: Scopus)
88.	Hirokazu Ito, Kazuyuki Matsumoto, Misao Miyagawa, Yumi Kuwamura, Yuko Yasuhara, Fuji Ren, Tetsuya Tanioka and Rozzano De Castro Locsin : Professional Nurses Attitudes Towards the Introduction of Humanoid Nursing Robots (HNRs) to the Hospital, International Association for Human Caring 36th International Conference, New Orleans, May 2015.
89.	Minoru Yoshida, Kazuyuki Matsumoto, Qingmei Xiao, Xielifuguli Keranmu, Kenji Kita and Hiroshi Nakagawa : Extracting Corpus-Specific Strings by Using Suffix Arrays Enhanced with Longest Common Prefix, Proceedings of the 10th Asia Information Retrieval Society Conference (AIRS 2014), LNCS 8870, 360-370, Kuching, Dec. 2014. (Summary) We propose a new term extraction algorithm that considers all of the substrings as term candidates.Our algorithm uses a suffix array as the data structure that emulates the suffix tree of the corpus.We use two scoring functions, one of which is used to detect good substring boundaries as linguistic chunksand the other is to find domain-specific phrases and combine them with a re-ranking approach.Experiments show that the proposed all-substring term extraction algorithm shows good performancefor highly-frequent terms compared with the baseline algorithm that uses a morphological analyzer in the preprocessing step. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-319-12844-3_31 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84912135895 (DOI: 10.1007/978-3-319-12844-3_31, Elsevier: Scopus)
90.	Kazuyuki Matsumoto, Sasayama Manabu, Qingmei Xiao, Fujisawa Akira, Minoru Yoshida and Kenji Kita : Reranking the Search Results for Lyric Retrieval Based on the Songwriters' Specific Usage of Words, Electronics, Communications and Networks IV Proceedings of the 4TH Internationa Conference on Electronics, Communications and Networks, 12 15 December 2014, Beijing, China, 1045-1052, Beijing, Dec. 2014. (Summary) In this paper, we proposed a method to search lyrics by considering tendency of the words used by the songwriters, and aimed to improve the search accuracy. The existing methods of query expansion for document retrieval tended to focus on the words co-occurrence relation obtained from large amount of text data or to focus on synonymy relation of words to expand query. However, lyrics have different characteris-tics from business documents or weblog articles. For example, lyrics contain a lot of expressions made with different combinations of words compared to other text styles. Therefore, it is not always effective to focus on co-occurrence relation or semantic relation on thesaurus. In this paper, we were on the assumption that the tendency of the words used in the lyrics are different among songwriters. And, we constructed a data of the important words for each songwriter. We used this important word list made for each songwriter to add weight on the search results by n-gram. According to the weights, we rearranged the rank order of the search result aiming to improve the accuracy. We conducted an evaluation experiment using the artificially-created fragment of lyrics including errors as search query, and found that the proposed method improved MRR compared to the baseline method. (Keyword) lyric retrieval / query expansion
91.	Kazuyuki Matsumoto, Fuji Ren, Qingmei Xiao, Minoru Yoshida and Kenji Kita : Emotion Predicting Method Based on Emotion State Change of Personae according to the Other's Utterance, Proceedings of the 3rd IEEE International Conference on Cloud Computing and Intelligence Systems(CCIS2014), 427-432, Nov. 2014. (Summary) It is a very difficult task to predict others emotion from dialogue flow for dialogue system or robot, however, that is a natural task for human.As the reason, because of the emotion of human changes at all times when he / she have a conversation with other people, these changes would be considered that oc-curred by responding internal emotional state to stimulus from external world .In the researches about dialogue system, most of them are superficial feature of utterance for question-answering system, thus, internal emotional state and stimulus from external world.In this paper, we focused on the scenario dialogue sentences. We annotated the various information to the corpora for helping analyze dialogue flow or human relationships and constructed the scenario emotion corpus. We discussed that the others utterances or emotions how effect to emotion changes by analyzing this cor-pora.As the method to predict the others emotion changes from self-emotion, we proposed the predicting method based on the emotion predicting weight. As the result of evaluation experiment, the effectiveness of the proposed method could be confirmed. (Keyword) emotion prediction / scenario corpus / emotion corpus (Link to Publication Site) ● Publication site (DOI): 10.1109/CCIS.2014.7175773 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84988227783 (DOI: 10.1109/CCIS.2014.7175773, Elsevier: Scopus)
92.	Minoru Yoshida, Kazuyuki Matsumoto, Kenji Kita and Hiroshi Nakagawa : Unsupervised Analysis of Web Page Semantic Structures by Hierarchical Bayesian Modeling, Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2014, Part II, LNAI 8444, 572-583, Tainan, May 2014. (Summary) We propose a Bayesian probabilistic modeling of the semantic structures of HTML documents.We assume that HTML documents has logically hierarchical structures and model them as links between blocks.These links or dependency structures are estimated via sampling methods. We use hierarchical Bayesian modeling where each block is given labels like ``heading''or ``contents'', and words and layout features (i.e., symbols and HTML tags) are generated simultaneouslybased on these labels. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-319-06605-9_47 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84901278613 (DOI: 10.1007/978-3-319-06605-9_47, Elsevier: Scopus)
93.	Akira Fujisawa, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Identifying who drew the illustration focusing on the eyes of the characters, Proceedings of 20th Korea-Japan Joint Workshop on Frontiers of Computer Vision, 82-89, Feb. 2014. (Summary) In this paper, we aimed to automatically identify who draw the of illustration image. Designs of illustration image differ depending on the illustrators. Also, it is difficult to find out common characteristics from the several illustrations drawn by illustrator. We tried to identify who drew the illustration by detecting distinctive habits of the illustrator from his/her works and by using them as the feature. To obtain the feature, we focused on the area of eyes of the illustrated characters. We obtained the edge information from there and calculated percentage of the edges. We call this percentage a "Line Percent" and considered it as the unique feature quantity of the illustrator. From an experiment, the average recall was 35 % and some of the recalls were over 90%. This result suggested that we could identify who draw the illustration images to some extent by using Line Percent as the feature. (Keyword) image processing / Identifying / Illustration / Edge / Canny Edge Detection
94.	Qingmei Xiao, Chen Mei, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Improvement of Indexing Methods for Audio Fingerprinting Systems, International Conference Data Mining, Civil and Mechanical Engineering (ICDMCME'2014), 65-71, Feb. 2014. (Summary) In music retrieval methods based on audio fingerprints, a large database is required in order to compare the fingerprints extracted from the query. It is necessary to develop an efficient search method. In this paper, we propose a method for index compression using a compressed suffix array. The proposed method compresses the index by encoding the 8-bit data sequences by Run Length Encoding. We also use Fibonacci Hashing function which can adjust the size of hash table according to the capacity of memory, provide a good distribution of hash value, and save memory.
95.	Mei Chen, Qingmei Xiao, Kazuyuki Matsumoto, Minoru Yoshida, Xin Luo and Kenji Kita : A Fast Retrieval Algorithm Based on Fibonacci Hashing for Audio Fingerprinting Systems, Proceedings of the 2013 International Conference on Advanced Information Engineering and Education Science, 219-222, Beijing, Dec. 2013. (Summary) In audio fingerprinting system, the database consists of hundreds of millions of sub-fingerprints. How to find out the most similar audio in the shortest time and use less memory in huge repository of sub-fingerprints is a hot topic of research. Recently, Philips introduced an effective search method based on hash table. In this paper, a fast retrieval algorithm based on the Fibonacci Hashing is proposed as an extension of Philipss method. The algorithm uses Fibonacci Hashing function which can adjust the size of hash table according to capacity of memory, provide a good distribution of hash value, and save memory. The performance of presented algorithm has been evaluated by experiments.
96.	Yunong Wu, Kenji Kita, Kazuyuki Matsumoto and Kang Xing : A Joint Prediction Model for Multiple Emotions Analysis in Sentences, 14th International Conference on Intelligent Text Processing and Computational Linguistics, 149-160, Mar. 2013. (Summary) In this study, we propose a scheme for recognizing people's multiple emotions from Chinese sentence. Compared to the previous studies which focused on the single emotion analysis through texts, our work can better reflect people's inner thoughts by predicting all the possible emotions. We first predict the multiple emotions of words from a CRF model, which avoids the restrictions from traditional emotion lexicons with limited resources and restricted context information. Instead of voting emotions directly, we perform a probabilistic merge of the output words' multi emotion distributions to jointly predict the sentence emotions, under the assumption that the emotions from the contained words and a sentence are statistically consistent. As a comparison, we also employ the SVM and LGR classifiers to predict each entry of the multiple emotions through a problem transformation method. Finally, we combine the joint probabilities of the multiple emotions of sentence generated from the CRF-based merge model and the transformed LGR model, which is proved to be the best recognition for sentence multiple emotions in our experiment.
97.	Yunong Wu, Kenji Kita and Kazuyuki Matsumoto : Recognition of Multiple Emotion States of Words using Multi-engine System, 11th International Conference on Computer Applications 2013, 201-206, Feb. 2013. (Summary) In this paper, we propose a multi-engine voting system to recognize the multiple emotion states expressed in the Chinese words according to the word identity and the context information, which can better reflect the people's real emotion states compared to previous works focused on single emotion analysis. Conditional Random Field and Maximum Entropy are employed to learn two probabilistic models to predict the possible emotions and assign the corresponding probability scores to the eight basic emotion types as well as the no-emotion type. To solve the problem of imbalanced data source, we introduce a threshold to improve the recognition of no-emotion and other word emotions. Our experiment achieves promising results which prove that multiple emotion states can be better recognized by the multi-engine system rather than either Conditional Random Field or Maximum Entropy (Keyword) Emotion Estimation / Maximum Entropy / Conditional Random Fields
98.	Yunong Wu, Kenji Kita and Kazuyuki Matsumoto : Recognition of Multiple Emotion States of Words using Multi-engine System, 11th International Conference on Computer Applications 2013, 201-206, Feb. 2013.
99.	Kazuyuki Matsumoto, Kenji Kita and Fuji Ren : Emotion Estimation from Sentence Using Relation between Japanese Slangs and Emotion Expressions, Proceedings of 26th Pacific Asia Conference on Language,Information and Computation, 377-384, Nov. 2012. (Summary) Most of Japanese slang words such as Wakamono Kotoba are analyzed as unknown word or segmented wrongly by the morphological analysis system. These problems are causing negative effect on sentiment analysis in text. These words generally have many varieties of notations and conjugations, and they lack versatility. As a result, many of them are not registered in the dictionaries, making morphological analysis more difficult. In this paper, we aimed to decrease such negative effects of Wakamono Kotoba for the accuracy of emotion estimation from sentence and proposed a method to increase the accuracy by using a classification method based on machine learning. In this method we used emotional expressions which had high relevance with Wakamono Kotoba as feature. As a result, the proposed method obtained 20% higher accuracy than the method only using morpheme Ngram as feature. (Keyword) Emotion Corpus / Japanese Slang / Out of Vocabulary
100.	Kazuyuki Matsumoto, Yoshioka Nobuhiro, Kenji Kita and Fuji Ren : Judgment of Depressive Tendency from Emotional Fluctuation in Weblog, Proceedings of the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - KES2012, 545-554, Sep. 2012. (Summary) Some people who have tendency toward depression describe their inner thoughts vividly in their weblogs. Such descriptions are sometimes made consciously but at other times unconsciously by the authors. If such unconscious authors could become aware of their own symptom of the depression, that would contribute to early detection and treatment of the sickness. To achieve this, it is essential to find out the tendency of depression from the descriptions in weblog. Because the symptom of depression is not static, we attempted to detect the tendency of depression focusing on the temporal fluctuation of emotion of the authors. As many weblogs describe what happened or what the authors felt in daily life like a personal journal, we thought that weblogs were ideal for obtaining the time-varying emotion of the authors. In this paper, we proposed the method to detect the tendency of depression in weblog. Our method is based on the temporal fluctuation of emotion described by the authors. (Keyword) Weblog / Emotional Fluctuation / Depressive Tendency
101.	Wu Yunong, Kenji Kita, Fuji Ren, Kazuyuki Matsumoto and Kang Xin : Exploring Emotional Words for Chinese Document Chief Emotion Analysis, Proceedings of 25th Pacific Asia Conference on Language, Information and Computation(PACLIC2011), 597-606, Dec. 2011. (Summary) In this study, we develop a document emotion analysis model by making use of the function of emotional words annotation. Eight basic emotion categories have been selected, including Expect, Joy, Love, Surprise, Anxiety, Sorrow, Anger and Hate, for both the word and the document level emotion analysis. We introduce two parameters, term relevance and term frequency, to evaluate the relations between the word emotions and the document emotions. Promising experimental results reveal the effectiveness of our document emotion analysis model under different emotion situations. (Keyword) emotional words annotation / document emotion analysis / term relevance / term frequency
102.	Yunong Wu, Kenji Kita, Fuji Ren, Kazuyuki Matsumoto and XIN KANG : Exploring Emotional Words for Chinese Document Chief Emotion Analysis, The 25th Pacific Asia Conference on Language, Information, and Computation (PACLIC25), 597-606, Dec. 2011.
103.	Wu Yunong, Kenji Kita, Fuji Ren, Kazuyuki Matsumoto and XIN KANG : Exploring Emotional Words for Chinese Document Chief Emotion Analysis, The 25th Pacific Asia Conference on Language, Information, and Computation (PACLIC25), 597-606, Dec. 2011.
104.	Kazuyuki Matsumoto, Kenji Kita and Fuji Ren : Emotion Estimation of Wakamono Kotoba Based on Distance of Word Emotional Vector, Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering, 214-220, Tokushima, Nov. 2011. (Summary) Recently, there are many research that aim at estimating emotion from text. The meaning of language expression that we use in daily life varies depending on the context used. In that sense, language expression conveys ambiguous information. Wakamono Kotoba are words used by young people, and they also have semantic ambiguity. Most of the Wakamono Kotoba are not registered in the existing dictionaries, making it difficult for a system to recognize the meaning of Wakamono Kotoba. In this paper, we propose a method for estimating emotion in Wakamono Kotoba that were not registered in the system, by using Wakamono Kotoba example sentences as features. The proposed method applied Earth Movers Distance to vector of words. As a result of the evaluation experiment, higher estimation accuracy was obtained by considering emotional distance between words -- an approach that had not been used in the conventional research -- than by using only word importance value. (Link to Publication Site) ● Publication site (DOI): 10.1109/NLPKE.2011.6138197 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84857271107 (DOI: 10.1109/NLPKE.2011.6138197, Elsevier: Scopus)
105.	Yunong Wu, Kenji Kita, Fuji Ren, Kazuyuki Matsumoto and XIN KANG : Modification Relations based Emotional Keywords Annotation using Conditional Random Fields, The 4th International Conference on Intelligent Networks and Intelligent Systems (ICINIS2011), 81-88, Nov. 2011.
106.	Wu Yunong, Kenji Kita, Fuji Ren, Kazuyuki Matsumoto and XIN KANG : Modification Relations based Emotional Keywords Annotation using Conditional Random Fields, The 4th International Conference on Intelligent Networks and Intelligent Systems (ICINIS2011), 81-88, Nov. 2011.
107.	Wu Yunong, Kenji Kita, Fuji Ren, Kazuyuki Matsumoto and Kang Xin : Modification Relations Based Emotional Keywords Annotation Using Conditional Random Fields, Proceedings of 2011 Fourth International Conference on Intelligent Networks and Intelligent Systems, 81-84, Oct. 2011. (Summary) In this study, we demonstrate the effectiveness of modification features for emotional keywords annotation on Chinese sentences extracted from a Chinese emotion corpus. Eight basic word emotion categories have been selected and Conditional Random Fields is employed in eight experiments with different sets of features. Our result shows that modification features make the contribution to emotional keywords annotation. (Keyword) modification features / emotional keywords annotation / CRFs
108.	Kazuyuki Matsumoto and Fuji Ren : Construction of Wakamono Kotoba Emotion Dictionary and Its Application, Computational Linguistics and Intelligent Text Processing, Vol.LNCS6608, 405-416, Feb. 2011. (Summary) Currently, we can find a lot of weblogs written by young people. In the weblogs, they tend to describe their undiluted emotions or opinions. To analyze the emotions of young people and what causes those emotions, our study focuses on the specific Japanese language used among young people, which is called Wakamono Kotoba. The proposed method classifies Wakamono Kotoba into emotion categories based on superficial information and the descriptive texts of the words. Specifically,the method uses literal information used for Wakamono Kotoba, such as Katakana, Hiragana, and Kanji, etc., stroke count, and the difficulty level of the Kanji as features. Then we classified Wakamono Kotoba into emotion categories according to the superficial similarity between the word and the Wakamono Kotoba registered in the dictionary with an annotation of its emotional strength level. We also proposed another method to classify Wakamono Kotoba into emotion categories by using the cooccurrence relation between the words included in the descriptive text of the Wakamono Kotoba and the emotion words included in the existing emotion word dictionary. We constructed the Wakamono Kotoba emotion dictionary based on these two methods. Finally, the applications of the Wakamono Kotoba emotion dictionary are discussed. (Keyword) Wakamono Kotoba / emotion dictionary / emotion corpus
109.	Nagai Yuko, Tetsuya Tanioka, Fuji Shoko, Yuko Yasuhara, Sakamaki Sakiko, Taoka Narimi, Rozzano De Castro Locsin, Fuji Ren and Kazuyuki Matsumoto : Needs and challenges of care robots in nursing care setting: A literature review, Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering, 292-295, Beijing, Aug. 2010. (Summary) This study aims to identify needs and challenges of care robot in nursing care setting through an extensive search of the literature. As the result shows, there exists a shortage of information about results of the introduction of care robots, the needs of recipients and care providers, and relevant ethical problems. To advance our research and to introduce care robots into setting, there are so many things to do; consider the application of natural language processing technology by collaborating with researchers in the robotics field, carry out an investigation, extract the needs, clarify ethical problems and seek solutions, conduct the on-site experiment study, and so on. (Link to Publication Site) ● Publication site (DOI): 10.1109/NLPKE.2010.5587815 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-78649300264 (DOI: 10.1109/NLPKE.2010.5587815, Elsevier: Scopus)
110.	Date Mai, Tetsuya Tanioka, Yuko Yasuhara, Kazuyuki Matsumoto, Yukie Iwasa, Chiemi Kawanishi, Hirai Eri and Fuji Ren : The present conditions, problems and future direction of the server-controlled clinical pathway system development in psychiatric hospitals, Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering, 296,303, Beijing, Aug. 2010. (Summary) Clinical pathways used today are paper-based, although many different kinds of clinical pathway are used in clinical practice. However, in the development of software for clinical pathway, it is difficult to achieve cooperation between medical experts, who are not used to expressing their ideas and work in words, and system-developers whose medical knowledge is limited. As a consequence, the current situation is that medical practitioners make their own software and use it in their practice. While this may work, it is less than ideal because refinements that may make the software most effective through engineering expertise is not used. Thus, in our research team, the nurse researchers and the engineering researchers cooperated and developed a clinical pathway system. In this paper, the present conditions, problems, and future direction of the server-controlled CP system development in the psychiatric hospitals, is discussed from viewpoint of nursing as a user. (Link to Publication Site) ● Publication site (DOI): 10.1109/NLPKE.2010.5587812 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-78649239475 (DOI: 10.1109/NLPKE.2010.5587812, Elsevier: Scopus)
111.	Yasushi Katsura, Kazuyuki Matsumoto and Fuji Ren : Flexible English Writing Support Based on Negative-Positive Conversion Method, Proceedings of IEEE NLP-KE2010, 499-505, Aug. 2010. (Summary) With development of the recent globalization, thechance to exchange in English increased in the business field.In particular, its necessary to write a thesis and a charterhandwriting in English. Because many Japanese are notused to making English sentence, it is a great burden towrite appropriate sentence in English without any supportfor creating English sentence. In this study we havedeveloped an English composition support system. By thissystem, its to search for the interlinear translation exampleto refer to by database and generate a new sentence byreplacing a noun in the example sentence. In this paper,based on the technique of Super-Function, we propose amethod to convert an affirmative sentence into negativesentence and vice versa to realize more flexible andextensive text conversion. (Keyword) Super-Function / English Writing System (Link to Publication Site) ● Publication site (DOI): 10.1109/NLPKE.2010.5587778 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-78649288880 (DOI: 10.1109/NLPKE.2010.5587778, Elsevier: Scopus)
112.	Kazuyuki Matsumoto, Yusuke Konishi, Sayama Hidemichi and Fuji Ren : Wakamono Kotoba Emotion Corpus and Its Application for Emotion Estimation, Proceedings of 2010 International Conference on Advanced Intelligence, Vol.2, 89-99, Beijing, Aug. 2010. (Summary) In this paper, we describe how we constructed the linguistic resources of Wakamono Kotoba emotion corpus to be used for emotion recognition and explain the features we obtained from statistical analysis. Our Wakamono Kotoba emotion corpus includes Japanese language used by young people and language for expressing emotion. These were mainly gathered from Weblogs that were written by young people from their teens to their twenties. (Keyword) Emotion Recognition / Wakamono Kotoba / Emotion Corpus
113.	Tetsuya Tanioka, A Kawamura, M Date, Kyoko Osaka, Yuko Yasuhara, Mika Kataoka, Yukie Iwasa, T Sugiyama, Kazuyuki Matsumoto, T Kawata, M Satou and K Mihune : Computerized electoronic nursing staffa daily records system in the Apsychiatric hospital: present situation and future prospects, Proceeding of IEEE2010, Aug. 2010.
114.	Kazuyuki Matsumoto, Junko Matsumoto and Fuji Ren : Construction of English Composition Support System Based on Conveying Emotion, Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering, 125-132, Dalian, Sep. 2009. (Summary) With the increased demand for English communication, various styles of learning support methods have been proposed and provided to the Japanese learners. However, there are still many learners finding it hard to read, write and speak in English. Regardless of language difference, understanding the other's intention and emotional status accurately and expressing what they think or feel to the others appropriately are the important abilities for actual communication. This paper focuses on the linguistic performance ability especially to communicate emotion and introduce our prototype system that help describe the emotion in English. (Keyword) English Composition / English Wrting / Natrural Language Processing / Emotional Expression (Link to Publication Site) ● Publication site (DOI): 10.1109/NLPKE.2009.5313802 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-72249109597 (DOI: 10.1109/NLPKE.2009.5313802, Elsevier: Scopus)
115.	Kazuyuki Matsumoto and Fuji Ren : Estimate Word Emotions Based on Part of Speech and Positional Information, The 5th International Conference on Intelligent Computing, Ulsan, Sep. 2009.
116.	Kazuyuki Matsumoto, Junko Minato, Seiji Tsuchiya and Fuji Ren : An Approach for Evaluating Emotion Tagged Corpus, IADIS International Conference Interfaces and Human Computer Interaction 2008, 258-262, Amsterdam, Jul. 2008. (Summary) This paper proposes a method for evaluating our emotion corpus and describes the result of evaluation. The emotion corpus was originally created in Japanese and English to be used for emotion recognition research and consisted of dialogue sentences including emotional expressions. To ensure the reliability and use the corpus as the standard of emotion evaluation, the objective evaluation is necessary because single person annotated tags on the corpus. For Japanese part of corpus evaluation, we conducted a questionnaire to the several examinees. The examinees were asked to tag arbitrarily emotion categories on the sentences in the corpus. The obtained sentence emotion tags were classified then the match rate of the initial tag and the tags annotated by the examinees. Reliability of each emotion tag annotated on the sentence was calculated and added as emotional weight on each sentence emotion tag. The 10-fold cross validation test was conducted using the weight added corpus to estimate sentence emotion and obtained recall of 0.62 on average. (Keyword) Emotion Tagged Corpus / Emotion Recognition / Corpus Evaluation
117.	Kazuyuki Matsumoto, Junko Minato, Seiji Tsuchiya and Fuji Ren : An Algorithm for Extracting Emotion Occurrence Sentence Pattern from Corpus, Proceedings of 2008 AIAC Symposium, 109-123, Tokushima, Mar. 2008. (Summary) In this paper, we propose a method of extracting sentence patterns for estimating speaker's emotion.Our method estimates speaker's emotion by using a dictionary that registered the sentence pattern extracted from sentences expressing speaker's emotion.Because sentence types expressing emotion are various, we focused on dialogue sentence that includes important elements such as subject or object and proposed a method to extract the sentence pattern automatically.This paper describes the accuracy for converting the sentence including emotional expressions into the sentence patterns.We conduct experiments for emotion estimation based on the sentence pattern then describe the result of the evaluation. (Keyword) emotion estimation / sentence pattern / automatic extraction / corpus
118.	Junko Minato, Kazuyuki Matsumoto, Seiji Tsuchiya, Shingo Kuroiwa and Fuji Ren : Comparison of corpus-based emotion estimation methods, Proceeding of the 10th IASTED International Conference Intelligent Systems and Control, 235-239, Massachusetts, Nov. 2007. (Summary) Internationalization of information is increasing needs for a computer to deal with multilingual communication. Understanding not only semantic information but also underlying emotion in textual information can contribute to assist such communication and also to make human-computer interaction more natural and congenial. For the purpose of emotion estimation from multilingual text, we created bilingual emotion annotated corpus in Japanese and English. Based on the corpus, we statistically analyzed the characteristics of how word emotion affects sentence emotion in each language. Two kinds of emotion estimation methods were proposed by using the existing techniques considering the feature quantity obtained from the corpus statistics then evaluation experiments were carried out. (Keyword) Corpus analysis / Affective computing / Statistic analysis / Emotion estimation
119.	Tetsuya Tanioka, Ai Kawamura, Kazuyuki Matsumoto, Kazushi Mifune, Yoichiro Takasaka, Kyoko Osaka, Shuichi Ueno, Rozzano De Castro Locsin, Mutsuko Kataoka, Takuya Matsuda, Fuji Ren and Toshiko Tada : Outcome Management and Morphologic Variance Analysis Using PsychomsTM for Patient Care in Psychiatric Hospitals, IEEE NLP-KE2007, 502-506, Beijing, Aug. 2007. (Summary) In 2003, the Japanese government set an approximate goal of 72,000 social hospitalization in-patients being discharged in 10 years from psychiatric hospitals to the community. To meet this challenge, the clinical pathway (CP) is a useful model for managing the team's progress in providing health care services. High quality discharge planning for patients requires continuous refinement of CP including the collection and analysis of variance data, i.e., information not listed in the CP model. The purpose of this study is to analyze variance data gathered using PsychomsTM to find unidentified issues with keyword sorting (keyword variance analyses system) and text-mining (text mining variance analyses system). Using morphological analysis to detect word occurrences in variance data, it may be possible to identify problems needing solution by developing automatic rankings to analysis outcomes. To achieve this, however, specialized experience is required for variance analysis. Also, there must be high congruence between the results of text mining variance analyses and keyword variance analyses systems.
120.	Junko Minato, Kazuyuki Matsumoto, Fuji Ren and Shingo Kuroiwa : Corpus Based Analysis of Japanese-English Emotional Expressions, IEEE NLP-KE2007, 413-418, Beijing, Aug. 2007. (Summary) Development of information technology has increased the chances of interaction between human and computer. Understanding not only semantic information but also underlying emotion will make communication with computer more natural and congenial. As preliminary research for emotion estimation from multilingual text, we focused on emotion expressions of Japanese and English and created a corpus with about 1,200 bilingual sentences and tagged emotions on words and sentences. Based on the corpus, we statistically analyzed the characteristics of how emotions were expressed in each language and also the relationships between sentence emotion and word emotion in the sentence. The results show the characteristics of emotion expressions in Japanese and English dialogue sentences. (Keyword) bilingual corpus / parallel corpus / emotion tagged corpus (Link to Publication Site) ● Publication site (DOI): 10.1109/NLPKE.2007.4368064 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-47749090495 (DOI: 10.1109/NLPKE.2007.4368064, Elsevier: Scopus)
121.	Kyoko Osaka, Tetsuya Tanioka, Rozzano De Castro Locsin, Shu-ichi Ueno, Kazuyuki Matsumoto, Chiemi Kawanishi, Shingo Kuroiwa, Seiji Tsuchiya and Fuji Ren : Electroencephalograph Estimation Method of Measuring `Empathic Understanding', IEEE NLP-KE2007, 514-519, Beijing, Aug. 2007. (Summary) It is important for health care staff to understand patients' emotion and affective changes. Health care staffs should understand the patients' world as if this was his her own. This is called "empathic understanding". To understand the emotional process, electroencephalograph recording (EEG) was performed on five healthy participants while watching the sentimental movie which was 24-minutes long. The EEG changes during the emotional scenes were collected and compared with emotionally stable states. By using power map and spectrogram analyses of EEG, alpha bands at the occipital region were seen specifically among the emotionally impressed states. It seemed that the changes indicated the objective expression of empathic understanding. We suggest that EEG with power map and spectrogram analyses is a useful fundamental tool for developing a second generation computer system adjusted to the changes of one's mind. However, further examinations to remove artifacts among the EEG results are needed. (Link to Publication Site) ● Publication site (DOI): 10.1109/NLPKE.2007.4368080 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1109/NLPKE.2007.4368080 (DOI: 10.1109/NLPKE.2007.4368080)
122.	Kazuyuki Matsumoto, Fuji Ren and Shingo Kuroiwa : An Emotion Estimation Method for Emotion Interface, Proceedings of International Symposium on Artificial Intelligence and Affective Computing 2006, 128-137, Tokushima, Nov. 2006. (Summary) The existing approaches of emotion estimation from a textwere mainly focused on estimating positive or negative emotions in thetext. However human emotions are more complex and we cannot judgeour emotions only by positive or negative. In this paper, we proposedan emotion estimation algorithm for conversation sentence. We gave therules of emotion occurrence to 1616 sentence patterns. In addition, we developeda dictionary which consisted of emotional words and emotionalidioms. The proposed method can estimate emotions in a sentence bymatching the sentence pattern of emotion occurrence and the rule. Furthermore,we can get two or more emotions included in the sentence bycalculating emotion parameter. We constructed the experiment systembased on the proposed method for evaluation. We analyzed weblog dataincluding 1645 sentences by the system, and conducted the experimentto evaluate emotion estimation accuracy. (Keyword) emotion interface / emotion estimation
123.	Kazuyuki Matsumoto, Tetsuya Tanioka, Fuji Ren, Yoichiro Takasaka, Takuya Matsuda, Toshiko Tada, Chiemi Kawanishi, Barnard Alan, Kyoko Osaka and Shu-ichi Ueno : Psychoms: A mental health patient management and variance analysis system using artificial intelligence, Proceedings of The Fourth International Conference on Information and The Fourth Irish Conference on the Mathematical Foundations of Computer Science and Information Technology, 73-77, Cork, Ireland, Aug. 2006. (Summary) Although it is agreed that there is a need for clinical pathway variance analysis, methods for creating a system are less well defined. The purpose of this paper is considering a way to develop the Psychoms that mental health patient management and variance analysis system using artificial intelligence. This system aims to improve a patient's quality of life through consistent and timely care and has potential to assist in the improvement of quality health care services. This article describes a method of outcomes management and variance analysis in this system and the prospective development of future research are discussed. (Keyword) Outcome management / variance analysis / nursing administration / team care / artificial intelligence
124.	Chiemi Kawanishi, Tetsuya Tanioka, Toshiko Tada, Fuji Ren, Kyoko Osaka, Kazuyuki Matsumoto, Shunji Mitsuyoshi, Rozzano De Castro Locsin and Shu-ichi Ueno : Consideration of Measuring Method for Empathic Understanding as Technological Competency of Nursing, Proceedings of The Fourth International Conference on Information and The Fourth Irish Conference on the Mathematical Foundations of Computer Science and Information Technology, 68-72, Cork, Ireland, Aug. 2006.
125.	Tetsuya Tanioka, Isao Nagamine, Fuji Ren, Kazuyuki Matsumoto, Keisuku Ueta, Lei Yu, Yoichiro Takasaka, Toshiko Tada, Fumiko Hashimoto, Yasuko Matsushita and Ruriko Yamashita : Research on development of the Hospital Management Outcomes Analysis System for using in psychiatric hospitals, Proceedings of the Ninth IASTED International Conference on Artificial Intelligence and Soft Computing, Vol.2005, No.1, 434-438, Benidorm, Sep. 2005. (Summary) Recently, clinical pathways (CP) are often implemented to improve care, yet their effect on quality of care and outcomes have not been generally evaluated. Especially, the item of CP and the outcome management for the patient with mental disorders is not examined enough in Japan. The purpose of this paper is to examine the development of prototype Hospital Outcomes Analysis System (HOAS). As the first step of HOAS development, authors considered to create CP and outcome management item for people suffering schizophrenia for the improvement of quality mental health care. Comprehensive survey interviews were conducted for the hospital staff. CP for the patient with schizophrenic and the critical criteria for patient outcome management (OMCP) were developed from these surveys. Additionally, in order to fully utilize CP, it is very important to develop the efficient and effective means for managing patient outcomes. The CP and outcome management in the hospital may bring patients and their families in progress QOL, in addition to job satisfaction of care-givers. As the second step, we developed a prototype HOAS based on OMCP during the whole period from hospitalization to discharge of which patient outcomes management is done.
126.	Kazuyuki Matsumoto, Junko Minato, Fuji Ren and Shingo Kuroiwa : Estimating Human Emotions using Words and Sentence Patterns, IEEE International Conference on Information Acquisition, Vol.1, No.1, 421-426, Hong Kong, Jun. 2005. (Summary) Our research group proposes a method to recognize and create human emotions which can be applied to a robot used for welfare services. We also aim to develop an affective computer with enough ability to communicate that prople don't sense a disconnect. In this paper, an "emotion estimation module" based on words for recognizing human emotions. This mudule extracts "emotion occurrence condition" based on emotions contained in words and sentence meanings, and estimates the emotion of a speaker or writer. The meterials used for the module are collected from written quotations from conversations and internet blogs. We constructed a prototype system based on the proposed method and conducted an experiment on sentences taken from internet blogs in order to verify the effectivenss of the method. (Keyword) emotion recognition / human emotion / welfare service
127.	Kazuyuki Matsumoto, Fuji Ren and Shingo Kuroiwa : An Algorithm for Measuring Human Emotions based on Context and Sentence Pattern, Proceedings of the third International Conference on Information, 215-218, Tokyo, Nov. 2004. (Summary) It is necessary to nurse the senior citizen for declining population of children and increasing nuclear family. We are researching and developing a welfare robot which has a sensibility to support the life of the senior citizen and to reduce nursing. One of the basic researches on such welfare robots is to measure the sensibility from conversation sentences. In this paper, an algorithm for measuring the sensibility is presented, and a prototype system based on the proposed algorithm has been constructed.

Proceeding of Domestic Conference:

1.	OGA Ryota, Kazuyuki Matsumoto and Minoru Yoshida : Hybrid Recommender System for Anime with Integrated Emotion Analysis from User Reviews, --- Improving Recommendation Precision and Catering to Diverse Tastes by Extracting Emotional Insights ---, 第26回日本感性工学会講演論文集, 1-10, Sep. 2024. (Summary) This study aims to develop a recommendation system for the anime market to help users navigate the overwhelming number of titles and find ones that match their preferences. As the anime market expands, discovering suitable titles has become challenging. The research introduces a hybrid recommendation system that leverages emotion analysis from user reviews, using the DistilBERT base model for emotion classification and incorporating this data into a matrix factorization-based algorithm. Five algorithms were developed and compared. By incorporating emotional information, the recommendation precision improved, highlighting the significance of emotional data. The emotion dictionary-based model effectively captures users' emotional preferences, indicating successful integration of emotional data. The findings demonstrate that emotion analysis significantly enhances recommendation accuracy, although further refinement is needed in how emotional information is utilized within the system. (Keyword) recommendation system / anime recommendation / emotion classification / user review
2.	Daiki Mori, Kazuyuki Matsumoto, XIN KANG, Manabu Sasayama and Keita Kiuchi : Development of an individually adaptable stress detection model using a stress management AI chat system, 第 23 回情報科学技術フォーラム(FIT2024)講演論文集, Vol.3, 357-360, Sep. 2024. (Summary) The aim of this research is to develop a stress management AI chat system that can connect users and counsellors. By developing this system and conducting user evaluations, we aim to confirm the effectiveness of the proposed method and contribute to the research and development of mental health care AI that can handle stress in an engineering way. This paper presents the results of preliminary experiments focusing on the construction of a chat system. The preliminary experiment was conducted for about three weeks during March 2024, and the results of the analysis of the data accumulated in the system during the experiment are discussed for the main experiment scheduled to be conducted after July 2024. (Keyword) stress management system / AI chatbot / stress detection model / personalization
3.	Daiki Mori, Kazuyuki Matsumoto, XIN KANG, Manabu Sasayama and Keita Kiuchi : Development of a Stress Management AI Chat System to Reduce Social Isolation, IT healthcare, Vol.19, No.1, 100-105, Aug. 2024. (Summary) This study aims to develop an AI chat system that can manage stress in order to support people who are isolated. The system aims to improve the efficiency of collecting data on stress factors and prevent the accumulation of stress through AI chat, as well as to support stress management by detecting stress and guiding the user to a specialized institution. This paper discusses the results of the analysis based on the data accumulated in the preliminary experiment conducted in March 2024, and discusses issues to be considered for the main experiment to be conducted in and after July 2024. The preliminary experiment was conducted on about 11 students in a laboratory for three weeks. The AI chat system changes the dialogue model every week, and the differences in the nature of the data collected in each case are compared to select the most appropriate dialogue model and to improve the model for this experiment. In particular, this time, the AI chat system is evaluated based on the average value of stress reduction in the post-chat questionnaire. (Keyword) Conversational AI / Mental Health Care / Stress Detection / LLM / Counseling Support
4.	Kotaro Kashihara, Toshiki Takanabe, Keita Kiuchi, Hidehiro Umehara, Koshi Irizawa, Masahito Nakataki, Shusuke Numata, XIN KANG, Minoru Yoshida and Kazuyuki Matsumoto : Constructing multimodal counseling dataset for depression detection and feature analysis, IT healthcare, Vol.19, No.1, 94-99, Aug. 2024. (Summary) In recent years, depression caused by stress and anxiety has been on the rise worldwide, and the number of people taking leave due to depression and other mental illnesses has been increasing in Japan as well. However, there is a serious shortage of counselors and psychiatrists, making early detection difficult. In this study, for early detection of depression, 25 Japanese university students were interviewed by a professional counselor for about 30 minutes, and a multimodal dataset consisting of verbal, audio, video, and heart rate data was constructed during the interview. Before and after the interviews, a questionnaire about the subjects' current psychological state was also administered. The constructed dataset was analyzed by extracting features using GiNZA, OpenSMILE, and OpenFace, and comparing them with the results of the questionnaire. In the future, we plan to conduct interviews with mentally ill subjects and collect data from them. (Keyword) multimodal dataset / depression detection / dataset
5.	KUMAMOTO Sho, Nakai Koga, Kazuyuki Matsumoto and Minoru Yoshida : A Citation Selection Method Based on Literature Feature Analysis to Support Writing Review Papers, Proceedings of the 38th Annual Conference of the Japanese Society for Artificial Intelligence, 1-4, May 2024. (Summary) This paper proposes a semi-automatic, efficient, and accurate method for selecting citations for systematic reviews. As a feature extraction method, we used similar sentence search to extract important descriptions and expressions that serve as citation criteria for systematic review articles, but some issues remain. We will examine the algorithm for citation selection by reviewing and narrowing down the methods of literature collection and constructing a classification model for selected and excluded papers in the systematic review. (Keyword) review paper writing support / text mining / systematic review / BiomedNLP-BiomedBERT / paternal postpartum depression
6.	HAYAMICHI Hirotaka, Kazuyuki Matsumoto, Ryota Nishimura and Minoru Yoshida : Improvement of health maintenance support system using a conversation robot by adding chat functions, Proceedings of the 38th Annual Conference of the Japanese Society for Artificial Intelligence, 1-4, May 2024. (Summary) We are developing a system that helps people improve their lifestyles by using an interactive robot and a web application that analyzes the content of the dialogue and suggests lifestyle habits. Using this system, we actually collected and analyzed lifestyle habits and evaluated the system, analyzing problems in the functionality and UI. Based on the results of this analysis, we aim to improve the system to make it more effective. This paper mainly describes the addition of a chat dialogue function and improvements to the analysis of user responses. The aim is to arouse the users interest in the system through the use of chat dialogues, to encourage continuous use of the system, and to motivate the user to improve his or her lifestyle. In addition, we confirmed that the use of GPT-3.5 in the analysis of user responses improves the accuracy of the Yes/No judgment of user responses. (Keyword) communication robot / health management system / chat dialogue / speech recognition
7.	石川将丈, 篠山学 and Kazuyuki Matsumoto : Anonymization of indirect personal information in dialog text, National Convention Record I.E.E. Japan, 40, Mar. 2024. (Summary) 近年様々な分野で，テキストデータの活用が進められており，個人情報の匿名化に対する需要が高まっている．個人情報には，それ単体で個人を識別可能な直接識別情報と複数組み合わせることで個人を識別可能な間接識別情報がある．既存のテキストデータに対する匿名化研究において，これらの違いに着目した研究は少ない．また，主な匿名化方法として，個人をk人以下に特定されないようにするk匿名化があるが，これは医療用データベースのように多人数の個人情報が含まれることを前提としており，1対1の対話テキストなどの個人情報が含まれている個人が少人数のデータに対しては有効でない．そこで本研究では，1対1の対話テキストに対して，間接識別情報を適切に匿名化することで，匿名化による情報量の減少を抑えた匿名加工情報の作成を目的とする． (Keyword) k-anonymization / indirect identifier / dialogue text
8.	時井駿, Kazuyuki Matsumoto and Minoru Yoshida : A Study on Improving Accuracy by Eliminating bias in Closed-domain Question Answering, National Convention Record I.E.E. Japan, 130-131, Mar. 2024. (Summary) 質問応答の分野においてクローズドメイン質問応答というタスクでは与えられたコンテキストから回答箇所を推測する．しかし，各データセットにおいて偏りが生じてしまう．本研究では，クローズドメイン質問応答システムの精度と汎用性を向上させるため，複数のデータセットの組み合わせとアンサンブル学習による効果の比較実験を行う．この目的のために，3つの異なるデータセットを使用し，事前学習モデルとしてBERTを採用する． (Keyword) closed domain question answering / bias / ensemble learning / EM score
9.	TAKANABE Toshiki, Kazuyuki Matsumoto, 木内敬太, XIN KANG, Ryota Nishimura and 篠山学 : 感情分析のためのカウンセリングマルチモーダルデータセットの構築および評価, 情報処理学会全国大会講演論文集, 4-83-4-84, Mar. 2024. (Summary) コロナ禍をきっかけとして，オンラインカウンセリングが急激に普及した．オンラインカウンセリングは，細かな表情や声色の変化の認識が難しいことが問題点として挙げられる．カウンセラーの判断を助け，業務効率化のために，クライアントの感情の自動分析が有効である．人間は相手の感情を読み取るとき，声色，表情，発話内容など複数の要素から総合的に判断を行っている．AIによる感情推定も同様に，複数の異なるモダリティを組み合わせて用いるマルチモーダル感情推定により，高精度な感情分析が可能になると考えられる．マルチモーダル感情推定には，学習用データとして，感情のラベルが付与されたマルチモーダルデータセットが必要となる．本研究では，オンラインカウンセリング中のクライアントの感情分析のためのマルチモーダルデータセットを構築し，その評価を行う．本データセットには，カウンセラーによる労務者を対象としたオンラインカウンセリングにおける労務者の様子を収録した動画と，第三者により付与された客観感情ラベルが含まれている． (Keyword) マルチモーダル感情分析 / カウンセリング面談 / フライスのカッパ係数 / ラッセルの円環モデル / オンラインカウンセリング
10.	高森翔琉, 篠山学 and Kazuyuki Matsumoto : Estimating Utterance Intentions in Interview dialogue, National Convention Record I.E.E. Japan, 41, Mar. 2024. (Summary) 対話システムがユーザと自然なやり取りをするためには，ユーザの発話意図を正しく理解する必要がある．発話意図を推定する手法として，発話文に対して二段階で分析をして発話意図を推定する手法 (1)などがある．これらの手法は修飾節等を含む発話文全文に対して適用される．発話意図は修飾節等がなくても推定できると考えられる．本稿では，発話文を構文解析し，述語動詞とそれにかかっている部分のみに対して発話意図を推定する手法を提案する．また，本研究では大規模言語モデルT5 (Text-to-Text Transfer Transformer)を使う． (Keyword) interview dialogue / utterance intention / Text-to-Text Transfer Transformer
11.	TANAKA Shunei, Kazuyuki Matsumoto, Sasayama Manabu and Minoru Yoshida : Music Retrieval System by Inputting Arbitrary Words Using Impression Space Mapping, 2024年電子情報通信学会総合大会講演論文集, D-12A-15, Mar. 2024. (Summary) 近年，インターネット上のサービスによる音楽の聴取がCDなどに比べて主流になっており，このようなサービスではパーソナライゼーションによる感性に基づいた楽曲推薦などが行われている．しかし，土橋[1]は『「自分の嗜好」を強く反映してしまうパーソナライゼーション』(土橋 2011:35)は情報選択の範囲を狭めることにもつながると指摘している．本研究では，楽曲探索をより広い範囲で行い，潜在的に好みの楽曲を検索するために，任意の語，すなわち自由に入力できるキーワードが表現する感性に基づいた楽曲検索システムの構築を目指す． (Keyword) 感性検索 / 楽曲検索 / 印象推定
12.	INOTANI Rurika, Kazuyuki Matsumoto, 篠山学 and Minoru Yoshida : Interview Dialogue System Introducing Praise, National Convention Record I.E.E. Japan, 62, Mar. 2024. (Summary) 近年，IT技術の進化に伴って対話型エージェントが普及しており，人間とAIのコミュニケーションが一般的になってきた．将来は，高齢者施設や病院で生活している人，独居生活をしている人などの話し相手として活躍することもあるだろう．このようなシチュエーションで，対話型エージェントが人に愛されるために，ユーザに親しみを持って対話してもらえることが期待されている．本研究では，ユーザの発話を褒めるインタビュー対話システムの構築を目指す．提案手法では，ユーザの入力した発話文テキストから褒める対象となる単語の抽出を係り受け解析により行い，テンプレート文に当てはめたものを入力としてBERTを用いたマスク単語の予測により褒め対象単語の選定を行う．予測された褒め対象単語を当てはめ，BERTに入力することにより出力された褒め単語の中から，より具体的な褒め表現を選択することで褒める文章を完成させる．アンケートによる評価の結果，提案手法により生成した褒め文章において概ね高評価を得ることができた．入力と出力に用いたテンプレート文の差異により生成される文章が不自然になる問題があったため，今後は同じテンプレート文を使用することで課題を解決したい． (Keyword) BERT / interview dialogue / Mask Language Model
13.	Kazuyuki Matsumoto : 情報リテラシー科目におけるAI構築課題の取り組み事例, 第29回FDフォーラム第3分科会大学教育におけるAIの活用ならびにその弊害, Feb. 2024.
14.	KUMAMOTO Sho, 中井恍我, Kazuyuki Matsumoto and Minoru Yoshida : A Method for Survey on Studies of Paternal Postpartum Depression Using Topic Modeling and Large Language Model, Shikoku-Section Joint Convention Record of the Institutes of Electrical and Related Engineers, 251, Sep. 2023. (Summary) 男性(父方)産後うつの課題解決に役立つ研究を概観するために，男性の産後うつに関連する論文を対象とした研究動向の調査が必須である．しかし，膨大な文献テキストから必要な情報を人手により取得し，把握することは困難である．本研究では，専門的な知識を有さない人でも瞬時かつ高精度に，取得したい文献情報にアクセスできる関連研究調査システムの構築を目指し，潜在ディリクレ配分法(Latent Dirichlet Allocation;LDA)と，大規模言語モデル(Large Language Model;LLM)を用いた研究動向抽出手法を提案する． (Keyword) topic modeling / postpartum depression / large scale language model / BioGPT / PubMedQA dataset
15.	Tsuyoshi Honda, Kohei Torii, Ryota Nishimura, Minoru Yoshida and Kazuyuki Matsumoto : Segmentation of Dental Panoramic X-ray Images Using the Deep Learning Model TransUNet, 第22回情報科学技術フォーラム (FIT2023), 571-572, Sep. 2023. (Summary) 歯科パノラマ X 線画像は歯の診療に使われる画像である．歯科パノラマの照合には専門的な知識が必要であり，歯科医師の負担軽減を目的として歯の自動診断システムの研究が行われてきた．その中で，深層学習技術を用いて歯の識別を行う状態分類モデルが開発されている．本研究では状態分類モデルの入力として必要な歯の輪郭情報を作成するため，深層学習技術を用いて歯科パノラマ X 線画像のセグメンテーションを行う．実験では，歯科クリニックで撮影，ラベル付けされたデータを取り扱う．セグメンテーションモデルには，近年医療セグメンテーションタスクで成功を収めているTransUNet モデルを用いて実験を行った． (Keyword) パノラマX線画像 / セグメンテーション / deep learning / TransUNet
16.	RYOTAROH Nagae, Kohei Torii, Ryota Nishimura, Kazuyuki Matsumoto and Minoru Yoshida : Automatic Classification Method for Dental Implants Using Deep Segmentation Model, 第22回情報科学技術フォーラム (FIT2023), 567-568, Sep. 2023. (Summary) 歯科インプラントとは，失った歯を補うための医療機器である．インプラントは，メーカーや種類が様々あり，それぞれで治療法が異なるため，歯科医師は治療時にインプラントの種類を把握しておく必要がある．本研究では，歯科医師の負担軽減を目的として，患者の歯科デンタル X 線画像から深層学習を用いたセグメンテーションモデルによってインプラントの領域を切り取り，画像分類モデルを用いてインプラントの分類を行う．切り抜きの精度は，IoU と Dice 係数でそれぞれ，0.923，0.857 となった．U-Net によってインプラントの領域を切り抜いた画像に対する分類の評価は，正解率，適合率，再現率，F 値がそれぞれ，0.851，0.852，0.884，0.840 となった． (Keyword) デンタルX線画像 / 画像認識 / deep learning
17.	Kenta Kumakura, Kohei Torii, Ryota Nishimura, Kazuyuki Matsumoto and Minoru Yoshida : Diagnosis of Teeth Condition in Dental Panoramic X-Ray Images and Validation of Generalization Performance, 第22回情報科学技術フォーラム (FIT2023), 563-564, Sep. 2023. (Summary) 近年, 医療ビッグデータの深層学習技術への活用が盛んに行われており, 医療ビッグデータから診療情報や画像, カルテ情報等を用いて診断結果の提示や診断援助等ができるようになってきた. 本研究では, 歯科医師の負担軽減と診断援助を目的として, 深層ニューラルネットワークアーキテクチャを利用した歯科パノラマ X 線画像における歯の状態診断を行う. 実験では, 共同研究先の歯科クリニックで撮影, ラベル付けされたデータを取り扱う. 歯は複数の状態を持つため, 分類モデルはマルチラベル分類手法を用いて実装する. 結果として, う蝕や根尖病巣の識別が困難であった. また, 未知の撮影機材由来のデータでは精度の低下が見受けられた. そのため, ドメイン汎化手法を用いて, さらなる検証を行った. (Keyword) パノラマX線画像 / 画像認識 / deep learning
18.	MOTONOBU Yoshida, Kazuyuki Matsumoto and Minoru Yoshida : Correction of inappropriate sentences using toxic sentence judging, 第22回情報科学技術フォーラム (FIT2023), 265-266, Sep. 2023. (Summary) 近年, SNS 利用者が急増しており, 8 割以上の人が使用しているという統計データもある. これに伴い, SNS 上で誹謗中傷や炎上などの被害に遭う件数も増加している.2020 年には某番組内でタレントがとった行動や言動への Twitter 上での誹謗中傷が, 結果的に自殺を幇助してしまった事例もある. 2022 年には回転寿司店での不適切行為に対して不特定多数の人たちが非難を浴びせる「私刑」と呼ばれる現象も問題となった.このような事件を回避する方法として我々は, 攻撃的な内容を含んだ文章を投稿前に検出し, 適切な文章に訂正するシステムを提案する. (Keyword) text correction / large scale language model
19.	Haruto Uda, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Investigation on Accuracy Improvement of Emotion Classification Based on Text Data, 第37回人工知能学会全国大会講演論文集, 1-4, Jun. 2023. (Summary) Recently, SNSs have facilitated the collection of a wide variety of text data. However, SNS text data has problems such as short sentences with abbreviations and colloquial expressions, which make labeling difficult, and the difficulty of collecting a large amount of data in a short period of time. To solve this problem, data expansion is an effective method for efficiently preparing large-scale, high-quality labeled text data for machine learning. In this research, we aim to improve the learning accuracy of sentiment classification by extending the data to Japanese texts. EDA (Easy Data Augmentation) was used as the data expansion method. In particular, the use of various models for text manipulation in the EDA increased the range of data expansion. The expanded text generated by data expansion was evaluated based on the semantic similarity and the degree of textual change. The optimal data for training was selected by determining a threshold value. The WRIME corpus was used as the dataset to ensure the reliability of the labels. In this presentation, we report the results of learning accuracy in sentiment classification using data expansion. (Keyword) data augmentation / easy data augmentation / sentiment analysis
20.	Kazuyuki Matsumoto, Ryota Kishima, Minoru Yoshida and Kenji Kita : コロナ禍前後におけるTwitterユーザの性格別感情変化の分析, 言語処理学会第29回年次大会発表論文集, 2365-2369, Mar. 2023. (Summary) Three years have passed since covid-19 pandemic began at the end of 2019, and the situation is changing daily as restrictions on behavior are gradually being eased. Even now, the dramatic changes in lifestyles continue to have a significant impact on people in terms of physical and mental stress. Our study analyzes the changes that occurred in people's statements, thoughts, and actions by conducting emotion analysis of the content of statements made on Twitter before and after corona virus pandemic, extracting characteristic expressions, and conducting a comparative analysis. In addition, we considered the changes in emotions by user personality type. (Keyword) social media / emotion estimation / COVID-19 virus pandemic / MBTI
21.	Manabu Sasayama, Keita Kiuchi, XIN KANG, Ryota Nishimura and Kazuyuki Matsumoto : インタビュー対話における問い返し文の生成に関する検討, 言語処理学会第29回年次大会発表論文集, 405-407, Mar. 2023. (Summary) 遠隔勤務をしているとき，遠隔労働者が自分自身のストレスに気づけないことも多い．精神面の健康状態が深刻になる前に自分自身で気づくことができれば，健康を維持することにつながる．本研究では，遠隔労働者が健康状態をセルフチェックするためのバーチャルエージェント対話システムの構築を目的としている．本稿では，対話時に必要となる「問い返し」の生成手法について考察する．
22.	Kazuyuki Matsumoto, 木内敬太, XIN KANG, Ryota Nishimura and 篠山学 : Relationships Between Client Stress and Multimodal Emotion Estimation in Counseling Situations, JSKE 第18回日本感性工学会春季大会講演論文集, Mar. 2023. (Summary) This study aims to develop a technology to detect the degree of stress of remote workers, which has been increasing in recent years, from multimodal data such as video and audio data during interviews. In this paper, we use interview data obtained in preliminary experiments to analyze the relationship between emotions estimated from facial, audio, and verbal information and stress evaluation by interview. Using the emotion estimation results for each modal based on machine learning algorithm as input, we train a model to predict stress evaluation results, evaluate errors, and confirm the validity of stress evaluation using the emotion estimation results. In addition, by clustering the time-series change patterns of the emotion estimation results, we will analyze whether or not there is a relationship between each type of emotion and stress evaluation. (Keyword) automatic stress detection / multimodal emotion estimation / remote worker
23.	Hirotaka Hayamichi, Kazuyuki Matsumoto, Minoru Yoshida, Ryota Nishimura and Kenji Kita : Development of health maintenance support system using spoken dialogue robots, HAI シンポジウム2023 講演論文集, Mar. 2023. (Summary) Lifestyle-related diseases account for 30% of all medical costs and 60% of all deaths. It is important to maintain good lifestyle habits to protect one's health. In recent years, an increasing number of services and devices allow voice input. Speech recognition makes it easier to use computers. In our study, we develop a system that helps people improve their lifestyle habits by using a web application that analyzes the content of conversations with an interactive robot and suggests lifestyle habits and other information. Using the developed system, we actually collected lifestyle habits, analyzed them, and evaluated the system. (Keyword) health maintenance support system / spoken dialogue robots / lifestyle habits
24.	TATSUYA Miki, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Highlight Extraction in Stories Considering Character's Emotional Movements, HCGシンポジウム2022発表予稿集, C2-7, Dec. 2022. (Summary) In this study, we propose a method for extracting and summarizing important sentences in novels by quantifying the magnitude and changes of characters' emotions. We selected five novels from the Aozora Bunko as research subjects and performed automatic summarization of each novel using the proposed method. We created correct answer data that was manually extracted sentences that express characters' emotions and evaluated the proposed method by precision and average precision of the top 10 sentences (Keyword) natural language processing / Extractive Summarization / emotion
25.	曽根大靖, 篠山学 and Kazuyuki Matsumoto : Development of a tool to freely convert sentences, 令和4年度電気・電子・情報関係学会四国支部連合大会講演論文集, 174, Sep. 2022. (Summary) 対話システムを構築するためには大量のテキストコーパスが必要になるが，その収集には時間がかかる．そこで，テキストコーパスを平叙文や疑問文などの文型へ変換することができると，収集にかかる時間を短縮できる．コーパスの量を増やすには機械翻訳を用いた翻訳再翻訳があるが，誰もが利用できる文型の変換ツールはほとんどない．本研究では対話システムのための文自在変換ツールの開発を行う．文自在変換ツールとは，平叙文や疑問文などの様々な文型の文章を互いに変換することができるツールの事である．平叙文肯定を基準として，それぞれの文型に対しての相互変換ができると，平叙文肯定を経由して全ての文型同士の相互変換が可能になる．必要な変換処理は6種類である． (Keyword) dialogue system / conversion tool
26.	Daiki Mori, Kazuyuki Matsumoto, 篠山学, Minoru Yoshida and Kenji Kita : Development of dialogue model reflecting individuality based on interview dialogue, 令和4年度電気・電子・情報関係学会四国支部連合大会講演論文集, 139, Sep. 2022. (Summary) 人間が AI とより親しみのあるコミュニケーションをするための要素の一つとして「個性」が挙げられる．AI に必要とされる個性は場面や状況に応じて異なるので，対話する相手に応じて個性を切り替えられるような仕組みが求められる．本研究では，日本語Transformer encoder-decoder対話モデルを使用し，インタビュー対話において異なる個性を持つ対話データごとにファインチューニングすることで対話モデルの個性に違いや特徴が出るかどうかを評価することを目的とする． (Keyword) dialogue system / dialogue model / interview dialogue
27.	TATSUYA Miki, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : 感情辞書を用いた物語のハイライト抽出, 第19回テキストアナリティクス・シンポジウム, Sep. 2022.
28.	Reishi Amitani, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Affective analysis and visualization from posted text, replies, and images for analysis of buzz factors, 第21回情報科学技術フォーラム講演論文集(ハイブリッド開催), Vol.2, 63-68, Sep. 2022. (Summary) 本研究ではTwitter上で起こるバズ現象について，ツイート及び添付された画像，ツイートに対して寄せられたリプライの感性を分析することにより探る．Twitter に投稿されるテキストの文字数には制限があるが，テキストのみで表現することが難しい情報には画像を添付することで視覚的に伝えやすくなる．また，バズ現象を分析するという観点からも，画像付きのツイートはテキストのみのツイートよりも分析に使える情報量が増えるため，より詳細な分析が可能になると考えられる．ツイートの事後情報であるリプライテキストも分析に用いることができる．リプライは，ツイート投稿後に，そのツイートに対する反応として投稿されるものであるため，具体的なバズ現象の要因分析につながると考えた．本研究では，画像付きツイートについて，ツイートテキスト，画像，リプライテキストからそれぞれ抽出した感性情報を分析することで，バズ現象の要因分析を検討する. (Keyword) buzz factor / information diffusion / Twitter / affective analysis
29.	DAICHI Kawahara, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : コメントデータと歌詞に基づく楽曲動画検索システム, Proceedings of the 36th Annual Conference of the Japanese Society for Artificial Intelligence, 4O1-GS-4-05, Jun. 2022. (Summary) Currently, video sharing services such as YouTube are used by many people. In these services, keyword search is the mainstream. In the case of keyword search, it is difficult to find a video that matches the user's interest unless appropriate words are used. In this study, We propose a method to retrieve music videos with similar impressions by analyzing the comment data given by viewers to music videos on YouTube and the lyrics of the music. In the proposed method, comments and lyrics are converted into vectors by Word2Vec, and the music videos with similar impressions are retrieved based on clustering by Fuzzy c-means. According to the mean reciprocal rank (MRR) scores, it was clear that the output of the music videos had the same impression within the top 3 songs. (Keyword) 楽曲動画 / word2vec / ファジィクラスタリング
30.	TOTTORI Kento, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Color Image Scale Reconstruction Using Multivariate Analysis and Machine Learning, JSKE, 35-38, Mar. 2022. (Keyword) color image scale / machine learning / multivariate Analysis / dimensionality reduction
31.	Naomichi Tabuchi, Kazuyuki Matsumoto, Minoru Yoshida, Ryota Nishimura and Kenji Kita : SNS の投稿内容に基づく生活習慣病発症リスクの分析, 言語処理学会第28回年次大会発表論文集, 1946-1950, Mar. 2022. (Summary) 国内における死亡原因は，がん・循環器疾患・糖尿病などで全体の約 6 割を占めている．これらの病気はいずれも食事・運動・睡眠などの生活習慣と深い関りがあり，生活習慣病と呼ばれている．生活習慣病には自覚症状がほとんどないため，病気の悪化に気づきにくいという特徴がある．本研究では，より簡易的に生活習慣病を予防するシステムの構築を目指し，ソーシャル・ネットワーキング・サービス(SNS)を用いて，ユーザの投稿内容から食事・運動・体調・精神状態に関するツイートの分析を行った．調査の結果，本研究の成果がユーザの投稿内容から生活習慣病発症の危険因子を検出するシステムの構築につながることが分かった． (Keyword) Social Networking Services / lifestyle-related disease / SNS / BERT / Latent Dirichlet Allocation
32.	Tatsuya Miki, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : 登場人物の感情表現文の抽出による物語のハイライト生成, 電子情報通信学会2022年総合大会情報・システムソサイエティ特別企画ジュニア&学生ポスターセッション予稿集, Mar. 2022. (Summary) 電子書籍の普及により数多くの文学作品をインターネット上で読むことが可能になった．しかし，読者がそれらの中から読む作品を選ぶことは容易ではない．本稿では物語について登場人物の感情が表現されている文を抽出することによる指示的要約の手法を提案する． (Keyword) 感情表現 / ハイライト生成 / 物語 / automatic abstracting
33.	竹嶋翔矢, 篠山学 and Kazuyuki Matsumoto : 機械学習を用いた単語の意味の分類, Proceedings of The 84th National Convention of IPSJ, Mar. 2022. (Summary) 近年，自然言語処理や画像処理に深層学習が盛んに利用されている．深層学習が使用されている有名なシステムには，Siri，Alexa，Googleアシスタントなどのスマートスピーカーやソフトバンクが開発した Pepperなどがある．それらのシステムは，深層学習の利用により実用化されたものである．特に最近，自然言語処理タスクで精度が高いとされているTransformersモデルは，マスク言語モデルと呼ばれるモデルであり，大量のテキストデータによる単語の穴埋め問題を事前学習タスクに用いている．また，BERTと呼ばれる，Transformersモデルをベースとして，自然言語処理において文脈を考慮したモデルがある．本研究で用いるGiNZAのELECTRAモデルは，BERTを改良したELECTRAをベースとしてGiNZAに組み込んだものである．深層学習により自然言語処理の多くの課題が解決しつつあるといえる．しかし，まだ解決できない問題も多い．現在，自然言語処理では，同じ綴り，同じ発音であっても文脈や前後の単語によって意味が異なる単語(以後，同綴同音異義語と呼ぶ)の解釈が難しい．本研究では，同綴同音異義語の意味の分類を目指す．例えば「頭」には「体の一部」と「物事のはじめ」の意味があり，文脈によって意味が異なる．このような語を意味ごとに分類する手法を提案する．また，GiNZA の通常モデルと文脈を考慮した ELECTRA モデルとを比較する. (Keyword) machine learning / 意味分類 / 単語
34.	Motonobu Yoshida, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : BERTを用いたSNS上における攻撃的文章訂正システム, 情報処理学会第84回全国大会講演論文集, Mar. 2022. (Summary) 現代社会において SNS の発展は凄まじく，ほとんどの人が使用しているという状況に置かれている．それに伴い，誹謗中傷や炎上などの被害にあう件数も増加している．2020 年には，某テレビ番組における出演者の行動や言動に対して，Twitter 上で誹謗中傷が多数投稿されたことから，自殺に追い込んでしまうといった事件が起きている．本研究では，SNS ユーザが発言を投稿する前に，投稿文の攻撃性を判定し，攻撃性を緩和した文章に変換して提示するシステムの構築を目指す． (Keyword) natural language processing / BERT / Social Networking Site / harmful expressions / toxic expressions
35.	Reishi Amitani, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : 投稿文および画像といいね数との関係に基づく流行要因分析, HCG シンポジウム2021, Dec. 2021. (Summary) 本研究ではソーシャルメディアの動向を調査し，Twitterでのバズ現象を支える要因を探るための分析手法を提案することを目的とする．Twitterに投稿されたテキスト内容だけではバズ現象の原因を特定できない場合があるため，画像が添付されたツイートに限定し，テキストと画像のそれぞれの特徴に関連性があるかどうか，また，これらの特徴の関連性が，人気の指標である「いいね」や拡散の規模を表す「RT」の数とどのように関連するかを調べた．画像とテキストから抽出した特徴量を入力とし，いいね数とRT数を出力した後，2つの入力(画像とテキスト)から同じ次元数の特徴ベクトルを中間層から抽出するためにマルチタスク学習型のニューラルネットワークを学習する．これらの特徴ベクトル間の距離といいね数との関係を分析した結果，BERTによる分散表現とInceptionResNetV2の特徴量がいいね数とRT数を予測する要因となり得ることがわかった． (Keyword) Multi-task learning / Buzz classification / Social media / Trend analysis
36.	Tsubasa Miyashita, Minoru Yoshida, Kazuyuki Matsumoto, Ryota Nishimura and Kenji Kita : Lifestyle Extraction from Corpus of Disease Fighting Blogs, Proceedings of 2021 Shikoku-Section Joint Convention Record of the Institutes of Electrical, Electronic, and Information Related Engineers, 187, Sep. 2021. (Summary) 闘病ブログは，患者の生活に関する行動情報(病状・食事・運動)について書かれているものがあり，疾患への理解や納得を得られるものも見られ，同病者の励みとなっている．闘病ブログコーパスとは，闘病ブログを収集し，記事のタグ付けを行ったものである．本研究では年々増加傾向にある2 型糖尿病患者による闘病ブログコーパスから，闘病活動に関わる生活習慣の分析を行う．この分析結果より，病状悪化の危険因子や病状改善の手がかりとなる要素を，非医療従事者が自然言語処理および機械学習手法を用いて発見し，闘病者や糖尿病予備軍の人々に助言できるシステムの構築を目指す． (Keyword) disease fighting blogs / lifestyle analysis / lifestyle-related disease / type 2 diabetes / text mining
37.	Reishi Amitani, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Buzz tweet classification based on texts and images, JSKE 第23回日本感性工学会講演論文集, 1-5, Sep. 2021. (Summary) As the information diffusion on social networking sites has become more common, content buzzes have become a frequent occurrence on the Internet via social networking sites. In marketing, it is important to know what is trending, and it is necessary to have a means of quickly detecting buzz on SNS. In this study, we define buzz tweets based on the number of likes and RTs on a tweet, and analyze the content of the tweets to obtain the characteristics unique to buzz tweets, so that we can predict buzz tweets in advance. In this study, we propose a classification method to predict whether a tweet is a buzz tweet or not, based on the statement of the tweet and the attached image. The results of the classification experiments show that the classification accuracy is higher when the statement of the tweet and the image are used as features than when only the content of the tweet and the image are used as features, indicating the effectiveness of the proposed method. (Keyword) buzz tweet / information diffusion / multitask learning / social networking site
38.	Takumi Matsunaga, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Extraction of Emotional Information from Distributed Representation Using Emotion Classification, 第20回情報科学技術フォーラム(FIT2021)講演論文集, Vol.2, 41-46, Aug. 2021. (Summary) 近年，自然言語処理の分野では単語の一般的ではない用法の検出など，単語分散表現から意味推定を行う技術が研究されている．それらの研究では分散表現全体から単語の情報を抽出しており，感性以外の情報が多く含まれている．本研究では感性情報のみを抽出することでその精度上昇を目指す．その手法として単語分散表現から感情分類を行うニューラルネットワークを作成し，ネットワークが学習した重みから感性情報が強く表れている次元の情報を抽出する．評価方法としては，コサイン類似度を用いて抽出次元同士が類似した単語の比較を行う. (Keyword) emotional information / distributed representation / neural networks
39.	Xiaohan Cai, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Facts analysis of food tweets, 情報処理学会第83回全国大会, Mar. 2021.
40.	Ryota Kishima, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Effectiveness of Emotional Information in Construction of MBTI Personality Estimation Model, 日本感性工学会春季大会(JSAEE2021)講演論文集, 1-6, Mar. 2021. (Summary) In recent year, anyone has become possible to interact with many people all over the world easily because of that SocialNetworking Services (SNS) such as Twitter and Instagram have become widespread. Many researchers are analyzing user's personalityand emotion using features such as text information and image information of SNS. Our study analyzes the effectiveness of usingemotional information as a feature when constructing a personality estimation model for Twitter users. For analysis, we use MBTI(Myers-Briggs Type Indicator), one of the widely used personality theories for personality indicators. (Keyword) Myers-Briggs Type Indicator / Twitter / emotion
41.	篠山学 and Kazuyuki Matsumoto : Taggingand Analysis of Utterance Intention Tag for Interview Dialogue Corpus, 人工知能学会_第91回言語・音声理解と対話処理研究会(SIG-SLUD-C003), Vol.B5, No.03, 56-61, Mar. 2021. (Summary) In this paper, we proposed the utterance intention tags for an interview dialogue corpus. We constructed an interview dialogue corpus with the tags we designed. Three or five annotators annotated the tags to the interview dialogue corpus (the total number of 49999 utterances) consisting of 30 dialogues. We conducted an evaluation experiment using Fleiss's kappa value to evaluate the reliability of the proposed tags. When three annotators annotated 18 different tags to the corpus, we obtained the kappa value of 0.55.
42.	hara kazuki, Kohei Torii, Kazuyuki Matsumoto, Minoru Yoshida, Ryota Nishimura, Jiro Tsuruki, 小林健一郎 and Kenji Kita : Tooth detection and segmentation from panoramic radiographs using Mask R-CNN, 令和2年度電気・電子・情報関係学会四国支部連合大会, No.15-9, Sep. 2020. (Summary) In Japan, the number of diagnostic items in dental diagnosis is considerably larger than in other countries, and it is necessary to reduce the burden of diagnosis on dentists because it is time-consuming for dentists to write dental formulae from scratch. Since interpretation of pantomime images is essential for diagnosis, understanding of dental health status, and planning of treatment, processing of pantomime images is an important issue for automation. In this study, tooth detection and segmentation from pantomime images are performed automatically. In this paper, we describe the method. (Keyword) medical image / deep learning
43.	fukuda tsubasa, Ryota Nishimura, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : A Study on the Analysis of Comics Using Deep Learning, 令和2年度電気・電子・情報関係学会四国支部連合大会講演論文集, Vol.15-7, 1, Sep. 2020. (Summary) The way we enjoy manga, a Japanese subculture, has been changing in recent years. In recent years, the way we enjoy manga, a Japanese subculture, has been changing. Looking at the sales trends in the market, we can see that e-books, which are read on electronic devices instead of paper books, are becoming popular. However, while smartphones and other small-screen devices are highly portable and easy to use, they are too small to display comic books, which are usually in paperback or A5 format, page by page. However, it is too small to display comics, which are usually in paperback or A5 size, page by page. If it is possible to extract and rearrange frames, it may provide an optimal reading environment for readers. In this study, we investigate the detection of coma regions to solve this problem.
44.	Akira Fujisawa, Kazuyuki Matsumoto, Kazuki Othta, Minoru Yoshida and Kenji Kita : ASCII Art Category Classification Based on Transfer Learning and Data Augmentation from Different Types of Image Corpus, JSKE 第22回日本感性工学会大会講演論文集, 1-7, Sep. 2020. (Summary) In this paper, we propose an ASCII art category classification method based on transfer learning and data augmentation.ASCII art is one of the nonverbal expressions that visually express emotions and intentions.There are similar expressions such as emoticons and pictograms. However, most of them are either represented by a single characteror embedded in the statement as an inline expression. ASCII art is expressed in various styles, including dot art illustration and lineart illustration. Basically, ASCII art can represent almost any object, therefore the category of ASCII art is very diverse. Many existing image classification algorithms use color information, however, since most ASCII art is written in character sets, there is no color information available for categorization. Create an ASCII art category classifier using the grayscale edge image and the ASCII art image transformed from the image as a training image set. We also use VGG16, ResNet-50, Inception v3 and Xception's pretrained networks to fine-tune our categorization. As a result of the experiment of fine tuning by VGG16 and data augmentation, an accuracy of 80% or more was obtained in the "human" category. (Keyword) ASCII art / Transfer learning / Fine tuning / Data augmentation / Domain adaptation
45.	Taiga Kirihara, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : テレビ番組視聴者のツイートからのキーワード抽出および分類, 2020年度人工知能学会全国大会(第34回), Jun. 2020. (Summary) 近年，若者を中心にテレビ離れが進んでいる．その主な理由としては，長時間番組であったり，視聴者の視聴時間が限られているためである．また，近年，ソーシャルメディアの発達に伴い，テレビ番組の視聴において，リアルタイムに意見や感想を述べる機会が増加した．実際にTwitter上では，いわゆる実況ツイートが盛んに行われている．そこで，本研究では，テレビ番組実況ツイートを用いてテレビ番組のイベントシーンに関するキーフレーズを抽出する手法を提案し，効率的なテレビ番組視聴を目指したシーン検索システムを提案する．実験の結果，番組時間に対して約5%∼10%の誤差で番組内容を推定した．また，抽出されたキーフレーズをt-SNEアルゴリズムによってイベントシーンカテゴリごとに視覚化した． (Keyword) 自然言語処理 / 情報検索 / Twitter
46.	Takumi Kojima, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : ツイート内容による夜間活動休止時間の推定に関する研究, 情報処理学会第82回全国大会講演論文集, 469-470, Mar. 2020.
47.	Shogo Kono, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : 文書分散表現を用いた音楽アーティストの特徴分類に関する研究, 情報処理学会第82回全国大会講演論文集, 477-478, Mar. 2020.
48.	Manabu Sasayama and Kazuyuki Matsumoto : インタビュー対話コーパスの構築と発話意図タグの設計, 情報処理学会全国大会講演論文集, 2-51-2-52, Mar. 2020. (Summary) 我々は，相手の良さを引き出すインタビュー対話システムの構築を目指している．そのための第一歩として本研究では，インタビュー対話コーパスの構築と発話意図タグの設計を行う．インタビュー対話コーパスは対談番組の字幕情報を利用したテキストコーパスとする．字幕情報を用いる利点として，番組の字幕制作部が作成していることから信頼性が高い点や対談番組であることから第三者が理解しやすい対話になっている点が挙げられる．発話意図タグは発話の元となる人間の欲求をベースに設計する．設計した発話意図タグを複数名でタグ付けする．最後に，一致率を計算して発話意図タグを考察する．
49.	Kazuyuki Matsumoto, Manabu Sasayama, Rei Terazono, Minoru Yoshida and Kenji Kita : Analysis of Utterance Intention and Kansei on Interview Dialogue Corpus, 日本感性工学会春季大会, 1-6, Mar. 2020. (Summary) In this study, we analyze interview dialogue corpora taken from an interview-type TV program, aiming at realizing aninterview dialogue system that brings out the best of the users of the system. After defining a tag set that classifies intention of utterance,these tags were added to six types of interview dialogue corpora, then we conducted utterance intention estimation using utterancesentence vectors obtained by using a pre-trained model. In the experiments, we analyze the expressions that are strongly related to theutterance intention, other important expressions, and the tendency of utterances of both the interviewers and the interviewees. (Keyword) Interview Dialogue System / Dialogue Act Prediction / Kansei Analysis / Corpus Analysis
50.	AIERKEN MAIERHABA, Kohei Torii, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : 深層ニューラルネットワークに基づくテキストと画像間のクロスメディア検索, 電子情報通信学会2020年総合大会学生ポスターセッション, No.ISS-A-076, Mar. 2020. (Keyword) deep learning / クロスメディア検索
51.	Kohei Torii, Kazuyuki Matsumoto, Minoru Yoshida, Jiro Tsuruki, 小林健一郎 and Kenji Kita : 深層学習を用いたパントモ画像からのインプラント体検出, 電子情報通信学会2020年総合大会学生ポスターセッション, No.ISS-A-060, Mar. 2020. (Keyword) deep learning / Panoramic radiographs / implant / Implant detection
52.	Kazuyuki Matsumoto : 徳島大学における人工知能を用いた共同研究の取り組み事例, 令和元年度第5回四国オープンイノベーションワークショップ講演資料集, Jan. 2020. (Keyword) artificial intelligence / deep learning
53.	Yuko Sakuta, Junichi Tsunobe, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Earth Mover's Distance を用いた画像の印象推定, 第21回日本感性工学会大会, No.13A2-02, Sep. 2019.
54.	Junichi Tsunobe, Yuko Sakuta, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : 深層学習に基づく配色イメージの感性マッピング, 第18回情報科学技術フォーラム (FIT2019), No.J-004, Sep. 2019.
55.	Naoya Fujino, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Word Mover's Distanceを用いたコーパス拡張による感情推定精度向上の検討, 言語処理学会第25回年次大会(NLP2019), 129-131, Mar. 2019. (Summary) 近年，深層学習をはじめとする機械学習はテキストマイニングや画像認識などの様々な研究分野で利用されている．今後の技術発展において必要不可欠であり，莫大な費用を投じている企業も少なくない．また，感情推定技術は人間とコンピュータ同士の円滑なコミュニケーションの実現に重要な役割を果たす．そういった中，よく問題点として挙げられるのが訓練データ量の不足や質の低下である．データ量の不足，質の低下は機械学習そのものの質の低下に直結する．本研究では，代表的な SNS の一つである Twitter に投稿されるテキストである「ツイート文」に着目し，そこに投稿されるツイート文を感情推定の対象とする．また，コーパス拡張が感情推定モデルの精度にどのような影響を及ぼすかを分析し考察する． (Keyword) Twitter / Word Mover's Distance / emotion estimation / data augumentation
56.	Yuuta Hada, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : リプライを用いたバズツイートの分類, 言語処理学会第25回年次大会(NLP2019), 237-240, Mar. 2019. (Summary) 近年 SNS の発達により，多数のユーザ間で口コミの拡散や共有が盛んにおこなわれるようになった．これによりインターネット上でコンテンツの流行が度々起こるようになった．ユーザ同士のコミュニケーションによっておこる流行のことを「バズる」と呼ぶ．この「バズる」ことを利用したマーケティングが重要視されており，いち早くバズに気づき取り入れることが今後のマーケティングで重要になってくると考えられる．既存研究において，時系列での拡散のされ方などから流行を検知するものがあるが，それらの手法では拡散された要因までは知ることができないという問題点がある．本研究では，Twitter においてバズったツイートに寄せられたリプライをベクトル化し，ツイートに対するリプライ全体を特徴ベクトルとすることで，「バズっている」特徴の定義を目指すとともにバズツイート内におけるタイプ別の分類分けの可能性について検討する． (Keyword) Twitter / reply / social media / buzz
57.	Hirokimi Fukuda, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Comment Mover's Distanceを用いた類似動画検索, IDRユーザフォーラム 2018, Nov. 2018. (Keyword) 動画コメント / Comment Mover's Distance / 類似動画検索
58.	Kazuyuki Matsumoto : sensibility analysis on social media and its application, TOKUSHIMA UNIVERSITY SCI-Tech Festival, Sep. 2018. (Keyword) social media / sensibility
59.	Kazuki Ohta, Kazuyuki Matsumoto, Akira Fujisawa, Minoru Yoshida and Kenji Kita : Classification of ASCII arts by Deep Convolutional Neural Networks, 平成30 年度電気関係学会四国支部連合大会講演論文集, 155, Sep. 2018. (Summary) 近年のSNS でのテキストコミュニケーションにおいて，顔文字，絵文字，スタンプといった非言語表現は感情伝達において重要であり，ショートテキストで円滑なコミュニケーションを行う際には欠かせないものとなっている．非言語表現のうち，アスキーアート(ASCII Art; 以下AA)は使用されている文字が特に意味を持たないことが多く，その配置や形状により視覚的に感情や意図を伝達，表現できる．しかし，それ故に使用されている文字を手がかりとした分類が難しい．本研究では，AA を画像化し深層学習を用いて分類する手法を提案する． (Keyword) ASCII art / classification / deep convolutional neural networks
60.	Kazuyuki Matsumoto, 藤澤日明, Minoru Yoshida and Kenji Kita : Comparison of Classification Methods for Ascii Art, 第32回人工知能学会全国大会講演論文集, 1-4, Jun. 2018. (Summary) In recent years, a lot of non-verbal expressions have been used on social media. Ascii art (AA) is an expressionby visual technique using characters. In this paper, we set up an experiment to classify AA pictures by usingcharacter features and image features. We try to clear which feature is more effective for the method to classify AApictures. We proposed four methods; 1) character frequency based method, 2) character importance value basedmethod and 3) image feature based method, 4) characters image feature based method. We trained the neuralnetworks by using these four features. As the experimental result, the best classification accuracy was obtainedwith the feed forward neural networks using characters image feature. (Keyword) ASCII Art / image feature / character feature / neural network
61.	Dilixiati Jirela, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Emotion Recognition from Emoticons based on Deep Neural Netwotks, 2018年電子情報通信学会総合大会ISS特別企画学生ポスターセッション予稿集, Mar. 2018. (Summary) Text communication has been developed in recent years, all kinds of daily contacts are through the SNS regardless of age, gender and active. As an advantage of text-based communication via the Internet. There is a point that you can quickly respond to anyone wherever you are. In this paper, by letting the deep convolution neural network learn the image features of emoticons. We propose a method to classify by emotion expressed by emoticons. By treating emoticons as images, it is possible to capture visual features and think that natural identification can be made rather than handling as character strings. (Keyword) emoticon / emotion recognition / deep neural networks
62.	Kazuyuki Matsumoto, Fuji Ren, Minoru Yoshida and Kenji Kita : 絵文字を手がかりとした俗語感情コーパスの拡張, 言語処理学会第24回年次大会発表論文集, 797-800, Mar. 2018. (Summary) 絵文字が含まれていない文からでも，絵文字を推定することができれば，感情推定が可能になると考える．本論文では，俗語と絵文字を両方含む多量のコーパスを文字レベルで学習させ，絵文字推定器を作成し，俗語を含んだ発話文コーパスへの感情タグ付け手法を提案する． (Keyword) emoji / slang / emotion / corpus
63.	片岡麻輝, Manabu Sasayama and Kazuyuki Matsumoto : Consideration on extension of lyric-emotion corpus, 人工知能学会第82回SLUD(言語・音声理解と対話処理)研究会予稿集, Vol.B5, No.3, 1-6, Mar. 2018. (Summary) Lyric emotion estimation can achieve song search which is based on emotions included in the lyrics. Whilst emotion expression dictionary is often referred on emotion estimation tasks, lyric emotions tend to be determined for each phrase. Therefore lyric emotion corpus containing lyric emotions which are for each phrase are needed. However, it often occurs that lyric emotion corpus stay small-scale since labelling to the corpus is done by human hand. In this work, we propose a method to extend a small-scale lyric emotion corpus by using word replacement based on distributed representation of words. In order to validate the proposed method, we also do an experimental lyric emotion classication with convolutional neural network with a lyric emotion corpus as the input. The corpus contains 5 classes of emotions. (Keyword) lyric / emotion / data augmentation / corpus / convolutional neural networks
64.	Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : プロフィール情報を用いたテキストの視点分類, IPSJ SIG Technical Report, Vol.2017-NL-234, No.11, 1-5, Dec. 2017. (Summary) 同一トピックに関する Twitter への投稿を，視点によって分類する手法を提案する．提案手法は，ユーザーのプロフィール情報を援用し，各投稿内容の分散表現を学習することで，類似する視点からの投稿をまとめる．
65.	Naoya Fujino, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Relation of Users Gender and Emotion Expression Tendency, Proceedings of the 31st Annual Conference of the Japanese Society for Artificial Intelligence, 2017, 1-4, May 2017. (Summary) In this study, we analyze the relation between emotion expressions of the Twitter users and their genders torealize emotion estimation adaptive to the users gender. We train a model to generate distributed representationsof sentences from a corpus divided according to gender. We train the features extracted with this model by using aneural network to construct an emotion estimator. The model constructed with our proposed method can estimateemotion with higher accuracy compared to the model without considering a gender. We also focus on slang andemoji as being related to emotion and analyzed their use tendencies to examine the difference between males andfemales. (Keyword) Twitter / emotion recognition / attribute
66.	Hirokimi Fukuda, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Index Generation from BGM by Using the Distinctive Comments Posted on the Songs, Proceedings of the 31st Annual Conference of the Japanese Society for Artificial Intelligence, 2017, 1-2, May 2017. (Summary) The purpose of this study is to generate indexes to the BGMs based on the distinctive comments annotated tothe BGM videos posted on Nico Nico Videos. Our proposed method detects the end/start positions of the BGMsconsidering the increase/decrease of the number of the comment data, the comments distinctive to the songs andthe exclamatory expressions annotated to the videos. The evaluation result showed that the proposed methodcould generate correct indexes within 10 seconds error for the BGM videos with length of less than 40 minutes andwith annotation of over 30,000 comments. (Keyword) index generation / Nico videos / distinctive comment
67.	Akira Fujisawa, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Classification method for large ASCII arts using image features, Proceedings of the 31st Annual Conference of the Japanese Society for Artificial Intelligence, 2017, 1-2, May 2017. (Summary) ASCII arts are visual expression that composed by strings. In the field of natural language processing, ASCII arts are treatedas a sequence of strings. In this paper, we consider to use shapes of ASCII art as features. Even if scale of ASCII arts and kindof characters that used to compose ASCII arts are different, proposed method enable to compare visual similarity. To extractshapes feature of ASCII art, we extract image features from ASCII art by converting ASCII arts to images. In the experiment,we compared visual similarity between ASCII arts and emoticons. As the experimental result, it was found that the imagefeature was effective to compare visual similarity even if the ASCII art and the emoticon used different kind of character (Keyword) ASCII art / image feature / classification
68.	Kazuyuki Matsumoto, Fuji Ren, Minoru Yoshida and Kenji Kita : 感情表出傾向の分析による対話破綻検出, 言語処理学会第23回年次大会発表論文集, 158-161, Mar. 2017. (Summary) 本研究では，ユーザの感情の変化に合わせてシステム自身の感情を表出させることができないために起こる対話破綻について，発話文から推測可能な感情傾向の比較により検出する手法を提案する．具体的には，発話文の分散表現ベクトルをもとに感情が表出されている強度を推定するモデルを用い，直前の 2 発話における推定結果との類似度の計算を行うことで，そのパターンを特徴量として対話破綻のラベルを推定する．評価実験では，提案手法を用いた対話破綻ラベルの推定をおこない，文の分散表現ベクトルの類似度パターンによる手法との比較をおこなう． (Keyword) 対話破綻検出 / 感情推定 / 分散表現
69.	Matsumoto Ryusei, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Visualization of the occurrence trend of infectious diseases using Twitter, 人工知能学会インタラクティブ情報アクセスと可視化マイニング研究会(第15回), 48-53, Mar. 2017. (Summary) The purpose of this research is to extract comments on infectious diseases from Twitterand to create an infection map.As a method,we check the facts of the extracted utterance,extractthe past tweets of the infected person from the result,and estimate the place where the personlives.We used SVMs both for the fact check and place estimation. (Keyword) Twitter / visualization / trend of infectious diseases
70.	Miyake Takeshi, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Flame prediction based on harmful expression judgement using distributed representation, 人工知能学会インタラクティブ情報アクセスと可視化マイニング研究会(第15回), 1-6, Mar. 2017. (Summary) In recent years, flaming on social media has been a problem. To avoid flaming, it is useful for the system to automatically check the sentences whether they include the expressions that are likely to trigger flaming or not before posting messages. In this research, we target two harmful expressions. There are insulting expressions and the expressions that are likely to cause a quarrel. Firstly, we constructed a harmful expression dictionary. Because a large cost requires to collect the expressions manually, we constructed the dictionary semi-automatically by using word distributed representations. The proposed method used distributed representations of the harmful expressions and general expressions as features, and constructed a classier of harmful/general based on those features. An evaluation experiment found that the proposed method could extract harmful expressions with accuracy of approx. 70%. On the other hand, it was found that the proposed method could also extract unknown expressions, however, it wrongly extracted non-harmful expressions at a rate of approx. 40%. (Keyword) harmful expression / distributed representation / Support Vector Machine / Flame prediction
71.	Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : 表形式からの分散表現獲得, 情報処理学会自然言語処理研究会研究報告, Vol.2016-NL-229, No.19, 1-6, Dec. 2016. (Summary) 表形式における単語埋め込み手法について考える．通常の文章を対象とした学習手法に比べ，表形式においては，単語の周辺文脈として，縦方向・横方向の二種類の文脈が考えられる．本稿では，これら同時に考慮した，二次元的なモデルを提案し，一次元的なモデルとの比較を行う．
72.	Fujioka Yuuki, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : 8bit 音源に対する自動採譜システムに関する研究, 平成28年度電気関係学会四国支部連合大会講演論文集, 17-5, Sep. 2016. (Summary) 本研究では，自動採譜分野において 8Bit 音源の特徴に着目して研究をする．8bit 音源とは，三角波と矩形波が二つの三和音とノイズからなる，一般的な音楽を形成するにあたる最低限の要素で構成された，シンプルな音源である．つまり，8bit 音源の採譜ができればベースやメロディーの動きが明らかになりその楽曲の骨組みが分かるようになる．従来は，自動採譜の分野の研究において，一般的なクラシックやポップスなど，大量の楽音が混在した楽曲データを対象としていた．1)それに対し，本実験では 8-bit 音源を対象にし，その最低限の和音のみで構成されたものを対象にすることによる影響について考察した． (Keyword) 8bit音源 / 自動採譜
73.	Nagai Hiroki, Fujisawa Akira, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Anime Character Classification Using Face Image based on Deep Learning, 平成28年度電気関係学会四国支部連合大会講演論文集, 178, Sep. 2016. (Keyword) Anime Character / Deep Learning / Face image / classification
74.	Nagafusa Kei, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Facial Expression Identification by Deep Convolutional Neural Networks, 第15回情報科学技術フォーラム講演論文集, Vol.3, 293-294, Sep. 2016. (Keyword) Deep Learning / Facial Expression / Deep Convolutional Neural Networks
75.	Fujisawa Akira, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : An Illustration Image Classification By The Style Focusing on Infrequent Colors, 第15回情報科学技術フォーラム講演論文集, Vol.3, 87-88, Sep. 2016. (Keyword) Illustration image / IF-hist / style
76.	松田紘伸, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Twitterを用いた病気の事実性解析及び知識ベース構築, 人工知能学会全国大会2016講演論文集, Jun. 2016.
77.	藤岡亮太, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Twitterから抽出したユーザの発言に基づく健康状態の推定, 人工知能学会全国大会2016発表予稿集, Jun. 2016.
78.	Satoshi Tanaka, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Personality Analysis Method of Twitter User for Information Recommendation, The Proceedings of the 30th Annual Conference of the Japanese Society for Artificial Intelligence, 2016, 1-4, Jun. 2016. (Keyword) personality analysis / Egogram / Twitter / information recommendation
79.	Akira Fujisawa, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Detecting emoticons in ASCII arts using image features, The Proceedings of the 30th Annual Conference of the Japanese Society for Artificial Intelligence, 2016, 1-2, Jun. 2016. (Keyword) ASCII arts / emoticon / image feature / Histogram Oriented Gradients / Scale-Invariant Feature Transform
80.	Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : 表形式のトピックモデルとその数値単位推定への応用, 情報処理学会自然言語処理研究会研究報告, Vol.2016-NL-226, No.16, 1-6, May 2016. (Summary) 表形式中の数値表現について単位が省略されている場合に，その単位を推定するための手法を提案する．Wikipedia 表形式中の，数値のみを含むセルを対象に，一行目のセル等の「周辺文脈」を利用し，適切な単位を推定する．また，表形式の外側の文章を利用するために，表形式と文章を同時にモデル化するためのトピックモデルを提案し，トピックの推定結果を単位推定に利用することで精度の向上を図る．
81.	Kazuyuki Matsumoto and Fuji Ren : 慣用句感情コーパスの構築および慣用句感情表現辞書の拡張, 言語処理学会第22回年次大会講演論文集, Mar. 2016.
82.	Fumihiro Iwasa, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Twitterユーザの属性別感情推定の検討, 言語処理学会第22回年次大会講演論文集, Mar. 2016.
83.	Yuuki Murakami, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : 深層畳み込みニューラルネットワークを用いた顔認識の精度評価, 2016年電子情報通信学会総合大会学生ポスターセッション, Mar. 2016.
84.	Masaya Matsuoka, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : トピック変動の分析による俗語の特徴抽出, IPSJ SIG Technical Report, Vol.2016-NL-225, No.4, 1-5, Jan. 2016. (Summary) 本研究では俗語の使われ方が時間経過によって変化することに着目した．俗語の含まれたツイート文の集合を潜在的ディリクレ配分法により分析し時間ごとのトピックを得る．このトピックにおけるトピック重要語の集合について隣接する時間(月単位)での類似度を計算し，トピックの変化を抽出し，俗語の特徴となるものを検討する． (Keyword) slang / トピック変動 / Latent Dirichlet Allocation
85.	杉本翼, 篠山学 and Kazuyuki Matsumoto : モダリティを考慮した歌詞検索のためのうろ覚えの歌詞の調査, Journal of Shikoku-Section Joint Convention of the Institutes of Electrical and Related Engineers, Sep. 2015.
86.	Yuki Murakami, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Shape-based image retrieval system for Two-dimensional Drawings, Journal of Shikoku-Section Joint Convention of the Institutes of Electrical and Related Engineers, 256, Sep. 2015.
87.	Takashi Matsuo, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : The optimal parameter estimation in the logo detection, Journal of Shikoku-Section Joint Convention of the Institutes of Electrical and Related Engineers, 256, Sep. 2015.
88.	Keisuke Hashimoto, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : A study on game music search by audio fingerprints, Journal of Shikoku-Section Joint Convention of the Institutes of Electrical and Related Engineers, 256, Sep. 2015.
89.	Ryuji Miyamoto, Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Sound Source Identification in Polyphonic Music Using a Mixed-sound Templates including Vocal Sound, Journal of Shikoku-Section Joint Convention of the Institutes of Electrical and Related Engineers, 268, Sep. 2015.
90.	Shimizu Hayato, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Cheating Detection by Using Correlation of Comment Distribution in Nicovideo, 第14回情報科学技術フォーラム講演論文集, Vol.2, 229-230, Sep. 2015. (Keyword) Nicovideo / Video Sharing Site / Cheat Video / Time series correlation analysis
91.	Okamoto Takuma, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : An Influence Estimation Method in Twitter Considering to Range of Influence, 第14回情報科学技術フォーラム講演論文集, Vol.2, 227-228, Sep. 2015. (Keyword) Twitter / Influence / Twitter API / Follower / Retweet
92.	Maekawa Kazuki, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Denoising techniques for flaming analysis of BBS, 第14回情報科学技術フォーラム講演論文集, Vol.2, 219-220, Sep. 2015. (Keyword) BBS / Denoising / Flame
93.	Yamada Tatsushi, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Category Classification of New Words Robust to Segmentation Errors, 第14回情報科学技術フォーラム講演論文集, Vol.2, 243-244, Sep. 2015. (Keyword) category classification / new word / segmentation error
94.	Hayato Shimizu, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Illegal Movie Detection by Using Comment Distribution Based on Temporal Correlation in NicoNico Douga, IEICE Technical Report, Vol.115, No.177, 97-101, Jul. 2015. (Summary) Recently, the video sharing site such as NicoNico Douga or YouTube are used for various use by individual and corporation. The authors of the video often make a profit by uploading video. However, because the video rating method is based on the frequency of looking and listening or the number of posted comments, the rating of the video can be intentionally increased by the author. We proposed the illegal video detection method by focusing on the correlation of temporal comment distribution rather than the frequency of looking and listening. The success detection rate was compared the proposed method with the baseline binary classification method by using machine learning based on the word feature. (Keyword) NicoVideo / comment distribution / temporal correlation
95.	Saitou Tatsuya, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Automatic Font Generation Based on On-yu, 情報処理学会全国大会講演論文集, 4-767-4-768, Mar. 2015.
96.	Maekawa Kazuki, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : 書き込みのエントロピーに着目した速報スレッドにおける盛り上がり要因の分析, 言語処理学会第21回年次大会発表論文集, 620-623, Mar. 2015. (Keyword) BBS / news thread / entropy
97.	Keisuke Yoshida, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : 顔文字のパーツの種類と表情の強弱に着目した顔文字表情推定, 言語処理学会第21回年次大会発表論文集, 449-452, Mar. 2015.
98.	Kazuyuki Matsumoto, Seiji Tsuchiya, Minoru Yoshida and Kenji Kita : 若者言葉の意味と感性を考慮した標準語への変換手法, 言語処理学会第21回年次大会発表論文集, 529-532, Mar. 2015. (Keyword) 若者言葉 / 俗語 / 印象 / 変換
99.	蜜石湧斗, 篠山学 and Kazuyuki Matsumoto : うろ覚えの歌詞における表記揺れの調査, Journal of Shikoku-Section Joint Convention of the Institutes of Electrical and Related Engineers, Sep. 2014. (Summary) 既存の検索システムでは，うろ覚えの可視をクエリとして歌詞検索をおこなうと表記ゆれや記憶違いから他の可視が出力され，求めている歌詞が得られないという問題点がある．本論文でぇあ，表記ゆれを訂正することを目的に表記ゆれの種類や傾向を詳しく調査し，分析する． (Keyword) 歌詞 / 表記揺れ
100.	Kohsuke Miyauchi, Kazuyuki Matsumoto, 篠山学, Minoru Yoshida and Kenji Kita : 歌詞検索に必要な内容語数の分析およびクエリ拡張手法の提案, Journal of Shikoku-Section Joint Convention of the Institutes of Electrical and Related Engineers, Sep. 2014. (Summary) 本研究は間違いの含まれる入力クエリの検索可能最低クエリ数を調べることにより検索システムの効率化や柔軟で強固な検索システムの作成の参考になるデータをとることを目的とした．誤り歌詞片を用いた評価実験の結果，必要な最小内容語数は平均4単語となった． (Keyword) 歌詞検索 / クエリ拡張
101.	Higashi Atsunari, Shun Watanabe, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : LDPC符号とハミング符号を用いた補助情報を伴う符号化システムの性能評価, 情報・システムソサイエティ・学生ポスターセッション予稿集, ISS-P-255, Mar. 2014. (Summary) 近年, インターネットの普及により通信する情報量が増加しているため, 情報を効率良く送信されるための情報圧縮技術が求められている. その中で, 1975年にWyner[1], Ahlswede and Körner[2]らが補助情報を伴う符号化システムの許容伝送率領域の証明した. しかし, 彼らの論文において誤り訂正符号などを実際に使用しての実験は行なわれていない. そこで本稿は, 補助情報を伴う符号化システムにおいて主符号化器にLDPC符号, 副符号化器にハミング符号を使用した場合の性能評価を行なう. (Keyword) LDPC符号 / ハミング符号
102.	Kanematsu Hideto, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : 知覚ハッシュによる類似画像検索, 情報・システムソサイエティ・学生ポスターセッション予稿集, ISS-P-256, Mar. 2014. (Summary) 近年，インターネットの普及により個人が触れることのできる画像データの量が膨大に増えている．それに伴い，画像から類似している画像を検索する類似画像検索の需要も増している．本研究では知覚ハッシュを使い，従来手法のグレースケールの代わりに3チャンネルのカラー情報を用いることで，人間の感性に近い類似画像検索を目標としている． (Keyword) Perceptual Hash / Block Mean Value Hash / Image Retrieval
103.	Okamoto Takuma, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : ナイーブベイズ法を用いたTwitterによる性格推定, 言語処理学会第20年次大会予稿集, Mar. 2014. (Summary) 本研究ではTwitterを利用し，Web上のツールを用いてエゴグラム分析を行ったユーザアカウントを対象に投稿内容を収集し，投稿内容に出現する単語を素性としてナイーブベイズ法による機械学習をおこない，投稿内容から性格の自動分類することを目的とする． (Keyword) ナイーブベイズ法 / Twitter / 性格推定
104.	Yamada Tatsushi, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : アーティスト評判分析に有効な特徴表現の抽出, 言語処理学会第20回年次大会予稿集, Mar. 2014. (Summary) 本研究では，n-gram出現頻度に基づき，アーティストに関するつぶやきデータから，評判分析に有効な特徴表現を抽出する．さらに，特徴表現に後続する単語を素性として，Support Vector Machine による機械学習によるカテゴリ分類をおこなう．評価実験の結果，アーティスト/カテゴリごとに差は出たものの，約80%の正解率で分類できることがわかった． (Keyword) text mining / マイクロブログ / Twitter / 評判分析
105.	Kazuyuki Matsumoto, Sasayama Manabu, Miyauchi Kohsuke, Minoru Yoshida and Kenji Kita : 歌詞における聞き慣れない表現と誤りとの関連性の分析, 言語処理学会第20回年次大会予稿集, Mar. 2014. (Summary) 本稿では，歌詞において特徴的な表現について，歌詞コーパスとWeb コーパスにおける出現頻度を比較することで分析する．また，聞き慣れない表現と誤りが起こるフレーズとの関連を，実際に楽曲の歌詞の書き起こしをおこなうことで得た歌詞片を用いることにより調査し，歌詞に特徴的な表現との比較もおこなう． (Keyword) 歌詞 / 単語親密度
106.	Sasayama Manabu and Kazuyuki Matsumoto : Query Expansion Using Semantic Information for Lyric Search System, HUMAN COMMUNICATION GROUP SYMPOSIUM 2013, Vol.HCG2013-B-1-3, 38-42, Dec. 2013. (Summary) A lot of sites such as a lyric search site which can search song titles and singer names using lyrics as a query, on the Internet now exist. These lyric search systems is suitable when the user remembers lyrics clearly. However, it is difficult to search song titles when the user vaguely remembers lyrics. Our aim is to develop the lyric search system which can search song titles by queries of mistaken lyrics. In this paper, as the first stage, we investigate dimly remembered lyrics. Next, we expand queries using a web corpus and a concept dictionary based on the investigation. (Keyword) lyric search / dimly remembered lyric / concept dictionary / web corpus
107.	Fujisawa Akira, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Identification method of author of illustration with using the face of the anime character, HUMAN COMMUNICATION GROUP SYMPOSIUM 2013, Vol.HCG2013-I-2-1, 523-530, Dec. 2013. (Summary) 本研究では，キャラクターを描いたイラスト画像に対し，顔部分に着目し，特に，目の部分からエッジ特徴量(Line Percent: LP)を抽出し，その書き込み具合をもとに，イラストレーター特有の特徴を見出して作者識別を行う．人による識別と，LPに基づく自動識別実験をおこない比較した結果，人による識別と同等とまではいかないが，ある程度の識別率を得ることができた．このことから，イラスト画像における部分的な書き込み具合を特徴量として用いることで，ある程度の精度での作者識別が可能なことがわかった． (Keyword) image processing / Illustration Image / Edge / Canny Edge Detection
108.	Akita Kyosuke, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Estimation of Intimacy between Characters in a Play Based on Response Rate, HUMAN COMMUNICATION GROUP SYMPOSIUM 2013, Vol.HCG2013-I-1-16, 511-515, Dec. 2013. (Summary) In this paper, we focused on the conversation sentences in a script and tried to obtain the relationships between the characters in the play. Generally, we understand the personalities of the characters and the relationships between them according to what they speak and how they behave in the story. Generally, we understand the personalities of the characters and the relationships between them according to what they speak and how they behave in the story. Therefore, we thought to use the emotional expressions in the dialogue sentences in the script to obtain the personalities of the characters and their relationships. There are many more elements except for emotional expressions in the conversations and we thought that such elements would be useful to predict interpersonal relationships. In this paper, we proposed a method to extract intimacy degree that is one of the most important elements in interpersonal relationships. We obtained the intimacy degree of the characters by calculating a response rate based on the speech frequency and the number of responses for the dialogue sentences between those characters. We constructed a corpus that collected dialogue sentences from the scripts and analyzed the effectiveness of the intimacy degree calculated by the proposed method by using the corpus. (Keyword) Scenario Dialogue Sentence / Response Rate / Intimacy Degree
109.	蜜石湧斗, Sasayama Manabu, Kazuyuki Matsumoto and Kenji Kita : Android Application for Lyric Search System Using Semantic Information, 平成25年電気関係学会四国支部連合大会講演論文集, Sep. 2013. (Summary) 我々は，入力する歌詞クエリの誤りに頑健な歌詞検索システムを開発することを目的とする．これまでに意味的なクエリの拡張を行い，意味的な類似度の高い順に歌詞を表示するシステムを実装した．本稿では多くのユーザに評価してもらうためユーザが利用しやすい Androidアプリを作成する．まず意味的情報を考慮したクエリの拡張について説明し，作成したアプリの構成について説明する． (Keyword) 歌詞検索 / Androidアプリ / 意味情報 / クエリ拡張
110.	Kyosuke Akita, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : 俗語の出現頻度と文末表現の関連性に関する分析, 平成25年度電気関係学会四国支部連合大会講演論文集, Sep. 2013. (Summary) 近年，スマートフォンなどの携帯型端末の爆発的な普及に伴い，Twitterやfacebookを始めとするSNSやブログサービスなどの利用者が増加している傾向にある．それにより，現在ネット上にはSNSなどから得られる膨大なテキストデータが存在している．これらのテキストデータでは，俗語と呼ばれているくだけた表現が多く用いられている．そのため，俗語が使用されているテキストデータを分析して情報抽出を行う場合，形態素解析誤りが影響し，信用できる結果が得にくい．しかし，現代の言葉を理解する研究は評判分析など様々な場所で応用できるものとして非常に重要であると考えられる．そのため，現代の言葉の特徴である俗語を分析し理解する研究は非常に重要であると考えられる．本論文では，ブログにおける文章の文末表現の混合率および変移率に着目し，俗語が表れやすい文章がどのような特徴を有するのかを分析する． (Keyword) 俗語 / 文末表現 / 混合率 / 変移率
111.	Gohda Sho, Shun Watanabe, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : Steganography using Coding with Cost Function, IEICE Technical Report, Vol.113, No.153, 5-9, Jul. 2013. (Summary) In recent years, the techniques that make communication safety are getting important as network communication advances. Cryptography cannot always meet this demand. Steganography can take measures to meet this situation. LSB substitution is the well-known technique of steganography. But it excludes the possibility of error bit generation, so that many techniquesbased on LSB substitution have been proposed. This paper propose the steganography using coding with cost function that convert embedded information to error pattern and embed it by exclusive or. Then this paper also shows that it can approach to theoretical bound asymptoticallyby numeric calculation. (Keyword) Information Hiding / Steganography / Coding with Cost Function / Embedded using Error Pattern
112.	Kyosuke Akita, Kazuyuki Matsumoto and Kenji Kita : 文字種と画数を用いた未知若者語の抽出, 言語処理学会第19回年次大会(NLP2013)予稿集, Mar. 2013. (Summary) 本研究では若者語は日常的に使用されやすい語を用いて生成されるという仮定に基づいて，語の表層的な特徴より文中の若者語を抽出することを検討する．文中からの抽出には条件付き確率場(以下CRF)を用いて確率モデルで文字列が若者語である確率の高い言葉を推定することにより行う． (Keyword) 単語抽出 / 条件付確立場 / 文字種 / 画数 / 俗語
113.	Ohnishi Tsubasa, Kazuyuki Matsumoto, Kenji Kita and Fuji Ren : Relation Analysis on Emotion and EEG Fluctuation in Dialogue, 平成24年度電気関係学会四国支部連合大会講演論文集, 95, Sep. 2012. (Summary) 脳波等の患者から発信される情報が重要なのはいうまでもないが，患者からの情報のみに頼るのではなく，介護を行う側の意識を変化させることが，より重要だと考える．本研究の最終目的は言葉が被介護者の感情にどのように影響するかを言語情報から予測することである．このような予測が可能になれば，患者の心を傷つけるような言葉を未然に防ぐことができる．本研究では，共感を分析しやすい対象として，演劇シナリオを用いることにした．演劇シナリオは，一般的な対話と比較すると，演出を重視しているためか，感情的な表現が多用されている特徴がある．介護場面などでは，演劇口調で話すことはほとんどないと考えられるが，本研究ではこうした感情表現の多用により，脳波に明確な変化が起きることを期待する． (Keyword) 対話分析 / 脳波変化
114.	Ryoh Ogo, Kazuyuki Matsumoto, Sasayama Manabu and Kenji Kita : Lyrics Search System Focused on Mishearing and Memorizing Mistake, 平成24年度電気関係学会四国支部連合大会講演論文集, 357, Sep. 2012. (Summary) 現在普及している楽曲検索において，アーティスト・曲名検索，歌詞入力検索ならば前者が主流である．実際には歌詞入力検索も同等の性能であると考えられるが，聞き間違いや記憶間違いを原因とした誤入力が検索精度を低下させる．本研究では，従来の言語研究のデータから使用者の入力ミス等の影響を最小限に抑え歌詞入力検索の精度の低下を防ごうと考えている．本研究では，歌詞の聴取実験により収集した誤り歌詞データを用いた分析をおこない，歌詞の誤りにどのように対処していくかを議論する． (Keyword) 歌詞検索
115.	Kazuyuki Matsumoto, Onishi Tsubasa, Kenji Kita and Fuji Ren : Construction and Analysis of Scenario Dialogue Emotion Corpus, 第11回情報科学技術フォーラム(FIT2012)講演論文集, Vol.2, 242-243, Sep. 2012. (Summary) 従来，言語・音声・画像といった情報からの感情推定を目的とした研究が多くの研究者によって行われてきたが，感情を表面的な情報から得るには限界がある．この理由として，感情は内在的な反応であるため，表情や行動を伴わないことも多いことがあげられる．しかし，発話者同士が親密な関係であればある程，感情の変化に伴う相手の言葉遣いの微細な変化に気付くことがある．このような言葉遣いの細やかな変化を検出できれば，対話における感情推定精度を向上できる可能性がある．徳久らは，発話文の文末表現に着目したが，言葉遣いの細やかな変化は，文末表現のみに表れるものではないと考えられる．たとえば，相手が普段使用しないような単語や慣用句を使用する場合でも，何かしら相手の心境の変化を察することもある．本研究では，話者の発話時における感情と，発話役割や文体との関係を調べる準備段階として，対話を記述したコーパスに感情，発話役割，文体タグの付与を行なった．このコーパスを分析することで，相手発話のどのような要素が影響して感情が変化するかを考察する． (Keyword) シナリオコーパス / 対話処理 / 感情推定
116.	Saito Narumi, Kazuyuki Matsumoto, Motoyuki Suzuki and Kenji Kita : Index Compression for Audio Fingerprinting Systems, 第11回情報科学技術フォーラム(FIT2012)講演論文集, Vol.2, 181-182, Sep. 2012. (Summary) 楽曲の曲名やアーティスト名，アルバム名などの楽曲情報を得るときには，楽曲検索システムが利用されている．マイクから楽曲の一部を読み込み，楽曲情報を得る技術にも使われている．多くの楽曲検索システムでは，オーディオ指紋(audio fingerprint)が使われている．オーディオ指紋は，楽曲の特定に用いられる特徴である．オーディオ指紋を楽曲情報データベースと共に用いることにより，楽曲の一部から楽曲情報を得ることができる．また，オーディオ指紋は楽曲を識別できることから，楽曲検索のみではなく，ネットワーク上に流通している著作権を侵害した楽曲の検出も可能である．効率的なオーディオ指紋検索には，接尾辞配列を利用した手法がある．これは，楽曲データベースの増大に比例して空間コストが増加する欠点を持つ．本稿では，データベースのインデックスを圧縮することにより，空間コストの削減を行う． (Keyword) audio fingerprint / information retrieval / 音楽検索
117.	Kazuyuki Matsumoto, 吉岡伸浩, Kenji Kita and Fuji Ren : うつキーフレーズと感情変動に基づくブログからのうつ検出手法, 言語処理学会第18回年次大会講演論文集, 1126-1129, Mar. 2012. (Summary) 我々は，ブログの書き手に，うつ傾向があることを自覚させることにより，うつ病の早期発見・早期治療に役立てることができると考えた．まず，うつ傾向の有無を，ブログ内容から推定することができるか否かをテキスト分類手法を用いた実験により明らかにし，どのような特徴がうつ判定に有効であるかを調査する．また，人間の心の変化をとらえるための特徴として，時間経過に伴う記述内容の変化から読み取れる感情状態の変化がある．多くのブログが，毎日の出来事や心情を時系列順に書き記したものであるため，そこから時間的変化を得ることが出来る．本稿では，うつ特有の言葉遣いを収集したうつキーフレーズ辞書に基づくうつ検出手法と，感情状態の変化に基づいたうつ検出手法を提案する． (Keyword) natural language processing / うつ検出 / ブログ
118.	吉岡直輝, Kazuyuki Matsumoto, Kenji Kita and Fuji Ren : 印象尺度に基づいた文章言い換え手法, 言語処理学会第18回年次大会講演論文集, 425-426, Mar. 2012. (Summary) 本稿では文中の単語を，同じ意味の，別の印象を持つ単語に置き換えることで，印象の違った文に変換する手法を提案する．友人や家族とコミュニケーションをとる際に用いるような単語の印象を，Casual(柔かい表現)，目上の人とコミュニケーションをとる際に用いるような単語の印象をFormal(堅い表現)の尺度と定義し，新聞記事とブログを基に，単語のCasual/Formalの印象付与を自動で行う．新聞記事は短い文で的確にニュースのポイントを読者に伝えるため，会話では使用されることが少ない漢字を多く用いる．このことから，新聞記事で多く用いられる単語をFormalな単語として分類する．ブログは個人的な体験や日記を掲載し，臨場感を出すためにくだけた会話表現を多く用いる傾向がある．このことから，本実験では，ブログで多く用いられる単語はCasualな単語として分類する． (Keyword) natural language processing / 言い換え / 印象
119.	平尾卓也, Kazuyuki Matsumoto, Kenji Kita and Fuji Ren : コーパスに基づく雑談を目的とした発話役割同定, 言語処理学会第18回年次大会講演論文集, 109-110, Mar. 2012. (Summary) 我々は，コーパスに基づく雑談を目的とした発話役割の同定の研究を行っている．西原らは発話役割の一部は助詞・助動詞の組み合わせで表されるとし，助詞・助動詞の意味を用いたルールによって発話役割の同定を行った．そこで本稿では品詞情報を用いた雑談の発話役割に有効な重みを模索し，コーパスに基づく雑談を目的とした発話役割の同定手法を提案する． (Keyword) natural language processing / 発話役割 / 雑談 / corpus / 形態素2-gram
120.	Kusumoto Yusuke, Kazuyuki Matsumoto and Kenji Kita : 脳波を用いた類似画像検索手法, Journal of Shikoku-Section Joint Convention of the Institutes of Electrical and Related Engineers, 4, Sep. 2011. (Summary) 本研究では，感性的画像検索を実現するため，脳波から抽出した相互相関係数を，感性特徴量として用いる．感性特徴量と画像特徴量を組み合わせて画像を検索することで，ユーザの感性に適合した検索結果を提示することを目標とする． (Keyword) electroencephalogram / filtering / information retrieval / 感性的画像検索 / information retrieval system
121.	Li Yang, Kazuyuki Matsumoto, Kenji Kita and Fuji Ren : Automatic Construction of Japanese-Chinese Word Dictionary on Super-Function Extraction for Japanese-Chinese Machine Translation, 第10回情報科学技術フォーラム講演論文集, Vol.2, 221-222, Sep. 2011. (Summary) 本研究ではスーパー関数を用いた日英機械翻訳の利点と問題点を考察し，スーパー関数に基づく機械翻訳を用いて日中機械翻訳システムの試作を行う．また，新たに提案したSFの変数の拡張についても述べる．提案手法では変数となる品詞の種類が多くなるため，訳語を適切に選択するために，参照する単語辞書の自動作成手法を提案する．最後に，提案手法による自動翻訳実験を行い，スーパー関数に基づく日中機械翻訳の精度を向上するには，対象とする例文に応じた対訳単語辞書が必要であることを明らかにする． (Keyword) machine translation / スーパー関数 / 日中機械翻訳 / 訳語選択 / 辞書構築
122.	Kazuyuki Matsumoto, Kenji Kita and Fuji Ren : Estimating Emotion of Wakamono Kotoba Based on Similarity of Example Sentences, 第10回情報科学技術フォーラム講演論文集, Vol.2, 281-284, Sep. 2011. (Summary) 若者言葉は，既存の語の言い換えたものであったり，省略表現も多いが，複合的な意味を持つ語や，既存の概念には属さない語も多い．こうした語のために，新語義を定義する必要があるが，その定義方法について未解決の問題が多い．我々の研究の目標は，若者言葉を含む文からの感情推定である．若者言葉を特徴として感情推定を行う際に，若者言葉を，その意味や用法，さらには感情などで分類することができれば感情推定の精度向上につながると考えられる．そこで，本稿では，若者言葉の用例に基づき，感情表現に関する情報を抽出することで，若者言葉の感情を推定する手法を提案する． (Keyword) 感情認識 / sentience computer / 若者言葉
123.	Kazuyuki Matsumoto and Fuji Ren : 感情推定における若者言葉の影響, 言語処理学会第17 回年次大会発表論文集, 846-849, Mar. 2011. (Summary) 若者言葉は，既知語に置き換えることが不可能な新しい概念の語や，既知語と同じ表記であるが若者言葉としてはまったく別の意味になる語も存在する．こういった語が感情を表現する場合，既知語に変換する方法を用いても効果が期待できない．そこで本研究では，若者言葉による感情表現の特徴を用例から調査する必要があると考え，若者言葉を含んだ文を収集することで，コーパスを構築し，分析を行う．そして，収集した文を用いた感情推定実験を行い，考察する．
124.	Hiroyuki Mitsuhara, Masami Shishibori, Hiroaki Ogata, Masao Fuketa, Hitoshi Tokushige, Kazuhiro Morita, Kazuyuki Matsumoto, Shun Watanabe and Momoyo Ito : Integrating Entertainment in Software Design and Experiment and its Effect, 日本教育工学会第26回全国大会講演論文集, 787-788, Sep. 2010.
125.	Katsura Yasushi, Kazuyuki Matsumoto and Fuji Ren : Expantion of Super-Function for Adjective to affect Noun, 情報処理学会全国大会講演論文集, Mar. 2010. (Summary) 本稿では，Super-Function(SF)に基づく機械翻訳を拡張するために，名詞にかかる形容詞を対象とした研究をおこなった．本研究で提案する手法は，名詞にかかる形容詞を名詞と合せて定数を減少させることで，重複したSFをデータベースから削除することで時間的・空間的コストを削減する． (Keyword) スーパー関数,機械翻訳
126.	Yoshioka Naoki, Kazuyuki Matsumoto and Fuji Ren : 英語科学技術論文執筆支援のための日英対訳例文データベース自動構築手法, 情報処理学会創立50周年(第72回)全国大会講演論文集, Mar. 2010. (Summary) 近年，英語科学技術論文の執筆の機会が分野を問わず増えている．本研究では，英語科学技術論文を執筆する際のサポートに用いることが可能な，日英対訳例文データベースの構築を目指す．このデータベースは，固有名詞や一般名詞などで，同一属性の語に置き換え可能な単語を，コーパスから学習した知識に基づき自動検出することで，それらの語の部分を変数として条件式に置換して構築される．本手法により構築した例文データベースは，論文作成支援のみならず，論文執筆の機会に乏しい大学生のための学習ツールへの応用にも有用と考えられる． (Keyword) 英作文支援,日英対訳データベース
127.	Ogawa Takuki, Kazuyuki Matsumoto and Fuji Ren : Emotion estimation for short sentence using word 1-gram as the feature, 情報処理学会創立50周年記念(第72回)全国大会講演論文集, Mar. 2010. (Summary) ユーザとの親和性の高い対話システムを実現するにはユーザの情緒面を考慮に入れて対応する必要がある．我々はユーザのつぶやきや一言からユーザの情緒を推定し，また，対話を始めるきっかけとすることでユーザの状態を考慮した自然な対話をおこなうエージェントを実現できると考える．しかし，つぶやきや一言に含まれる単語数は少なく，含まれる情報が少ないため感情推定が困難である．また，これまで短文に特化した感情推定手法は存在しない．本研究では，短文に含まれる単語や記号の持つ言語的意味と文章に表される感情との関係に着目し，単語1-gramを素性として，Webサービス「えもにゅ」に投稿された短文のコーパスを用いて統計的手法により感情推定をおこなう． (Keyword) 感情推定 / 単語N-gram / 単語1-gram
128.	Kurihara Takeru, Kazuyuki Matsumoto, Seiji Tsuchiya and Fuji Ren : Semantic Analysis of Metaphor "A-no-B" based on Sememe, IPSJ SIG Technical Report, Vol.2010-NL-195, No.1, 1-6, Jan. 2010. (Summary) The metaphor expression is used in various scenes that treat the natural language like the newspaper articles and the daily conversation, etc. We can express our intention by using metaphor expressions. However, it is very difficult to analyze metaphor expression by a computer. This paper constructs two kind of metaphor dictionaries. We also analyze meaning of metaphor"A-no-B" by searching metaphor dictionaries. Using the method described in this paper, we showed that this method was able to analyze meaning of metaphor A-no-B" with accuracy about 70%. (Keyword) 比喩 / 隠喩 / 意味素 / AのB
129.	Takuki Ogawa, Kazuyuki Matsumoto and Fuji Ren : About emotion estimation of Emonyu short sentence, IPSJ SIG Technical Report, Vol.2010-NL-195, No.2, 1-6, Jan. 2010. (Summary) This paper proposes a SVM-based emotion estimation method from a short message or a word by using word 1-gram as feature and use contribution of "Emonyu" as a corpus. "Emonyu" is a web service to which users contribute a short message or a word with a emotion mark.Therefore, the corpus using"Emonyu"contribution enables reflect writer's emotion directly,and reduce work of adding emotion tags to sentences of corpus. We use word 1-gram as feature is,in short sentences like a short message or a word including a few features,a writer generally express emotion using a linguistic meaning of a word or a mark and it is necessary to reduce a number of kinds of feature to get exact appear frequency of features. The result of experiments show that the F-measure of proposed method is higher than the F-measure of method using word 2-gram as a feature in all emotions.
130.	Katsura Yasushi, Kazuyuki Matsumoto and Fuji Ren : Flexible English Composition by Negative-Positive Conversion, 電気学会電子·情報·システム部門大会論文集, 1513-1515, Sep. 2009. (Summary) With development of the recent globalization, the chance to exchange in English increased in the business eld Inparticular, it's necessary to write a thesis and a charter handwriting in English. Because many Japanese are not usedto the making of the English sentence, a burden is big without a thing such as the making assistance tool, and it isdicult to write an appropriate sentence in English. We have developed an English composition support system. Bythis system, It's to search for the interlinear translation example to refer to by database and generate a new sentence byreplacing a noun during the example sentence. In this paper, based on the technique of Super-Function, the techniqueof carrying out mutual conversion of an positive sentence and negative sentence is proposed, and enables more exibletext conversion. Conversion to a negative sentence from an positive sentence can be enabled by this technique, and canwiden width of the text conversion. (Keyword) English composition / composition support / Super-Function / negative sentence / mutual conversion
131.	吉岡伸浩, Kazuyuki Matsumoto and Fuji Ren : Detecting Cause of Authors Plesure by Blog Information Analysis, 電気学会電子·情報·システム部門大会論文集, 1446-1448, Sep. 2009. (Summary) Some people are writing blog now.Blog is often written by writers subjective idea, and there is a person whowrites his daily life, too.On the other hand,by work or personal relationships,stress by the over work,I become the depression and there is a person who writes sentences that hint the suicide to blog.Healing is needed in order to reduce the stress and worries of these people.However,healings to be pleased with by a person are different.Therefore,it is necessary to detect with what the writer is pleased.Therefore,in this paper,to propose methods,whether the writer of the blog is pleased to use N-gram frequency dictionary and emoticon and sign dictionary more than usual is judged and to detect the cause of the pleasure of the writer. (Keyword) weblog / pleasure / emotion
132.	Xia Ling, Kazuyuki Matsumoto and Fuji Ren : Question Classification in Chinese Cuisine QA System, 平成20年度電気関係学会四国支部連合大会論文集, 433, Sep. 2008. (Summary) This paper presents an integrated method on question classification for Chinese cuisine question answering (QA) system. First, we exploit the domain knowledge to enrich question preprocessing, then classification features are extracted by means of domain attributes and the rule-based classifier is constructed. Support vector machine (SVM) classifier is used for secondary classification to the questions which cannot be matched with rules. A prototype system based on the proposed method has been constructed and an experiment on 453 natural language questions collected from Internet has been carried out. It achieved an accuracy of 96.22%. Result shows that a small number of linguistically motivated domain features can efficiently classify questions of Chinese cuisine QA system. (Keyword) QA system
133.	Kazuyuki Matsumoto and Fuji Ren : Construction of User-Oriented Intelligent English Composition Support System, 平成20年度電気関係学会四国支部連合大会論文集, 313, Sep. 2008. (Summary) 本研究では，スーパー関数を応用した知的英作文支援システムを構築する．利用者がベースとなる英文テンプレートを選ぶとき，利用者の検索意図に合致したものが出力できるように，キーワードの自動付与と，スーパー関数による日英対訳対応付け方法による例文提示手法を用いる．なお，提案システムは，利用者の記憶を辿りやすくするため，英文検索履歴や英文作成履歴を登録し，選択した訳語の選択履歴を時系列順に保持することで，まさに今作成しようとしている英文を書くための支援を可能とする． (Keyword) 英作文支援
134.	Tanimoto Yuki, Kazuyuki Matsumoto and Fuji Ren : Collection and analysis of Japanese-English idiomatic expression for construction of English composition support system, 平成20年度電気関係学会四国支部連合大会論文集, 282, Sep. 2008. (Summary) 本研究では，英作文支援システムにおいて，よく利用されるであろう慣用表現の用例をデータベース化し，その有効性について分析をおこなう．日英の対訳慣用表現辞書は，一般に，一方の言語のみを知っているだけでは，その使い道が分かりづらい点がある．そのため，対訳関係を登録しておくだけでなく，意味や感情といった，慣用表現自体が持つ役割をデータベースに登録しておくことで，より簡便で直感的な検索を実現することを目的としている． (Keyword) 慣用表現 / 英作文支援 / 日英慣用表現
135.	Katsura Yasushi, Kazuyuki Matsumoto and Fuji Ren : Japanese-English Bilingual Database Construction in English Composition Support System, 平成20年度電気関係学会四国支部連合大会論文集, 314, Sep. 2008. (Summary) 本研究では，英作文支援システムにおける日英対訳データベースを構築するための手法について提案する．我々の提案する英作文支援システムは，ユーザが書きたい英文のテンプレートをテーマ別に登録したテンプレートデータベースを参照し，その例文を手作業で編集していくシステムである．本システムの核となる技術として，スーパー関数形式によるテンプレートの自動登録手法と，スーパー関数内の訳語の自動推薦手法がある．本稿では，これらの技術を実現するために必要な要素を日英対訳テンプレートに登録するための手法について述べる． (Keyword) Super-Function / 英作文支援 / 日英対訳データベース
136.	Aoki Yui, Kazuyuki Matsumoto and Fuji Ren : Keyword automatic classification in the English composition support system, 平成20年度電気関係学会四国支部連合大会論文集, 281, Sep. 2008. (Summary) 本研究では，英作文支援システムにおけるキーワードの自動分類をおこなうため，ルールベースの手法を提案する．キーワードに関連付けられた複数のサブキーワードの集合から，ひとつのキーワードを選定するために，キーワードとカテゴリ間の関連付けを人手によりおこない，この関連付けに基づき，階層的な構造をもつカテゴリのなかから，キーワードのカテゴリのパスを1つに定める． (Keyword) 自然言語処理 / 英作文支援 / キーワード分類
137.	Kazuyuki Matsumoto, Junko Minato, Seiji Tsuchiya and Fuji Ren : 日英対訳感情表現コーパスに基づく感情表現抽出手法の提案, IPSJ SIG Technical Report, Vol.2008, No.NL-184, 69-75, Mar. 2008. (Summary) This paper statistically studies the emotional features of Japanese and English based on an emotion annotated parallel corpus and proposes a method for extracting emotional expressions. The proposed method estimates the emotion category of the emotional expressions by focusing on the three kinds of features: part of speech of emotional expression, position of emotional expression and part of speech of the previous/next morpheme of the target emotional expression. The evaluation experiment resulted over 90.0% (joy, hate) of accuracy in the method based on part of speech features. (Keyword) Sensibility information processing / Emotion tagged parallel corpus
138.	Kazuyuki Matsumoto, Seiji Tsuchiya, Ryosuke Sumitomo, Shingo Kuroiwa and Fuji Ren : 感情表現主体判定に基づく話者感情推定, Journal of Shikoku-Section Joint Convention of the Institutes of Electrical and Related Engineers, 283, Sep. 2007. (Summary) 本研究では，複数話者間における会話からの感情推定において重要となる，感情表現主体の判定手法を提案する．感情は一般的には，対人感情と自我感情に分けることができるが，それらの感情の判別には，発話文の主語が何であるかが重要となる．本研究は，人称代名詞に着目し，人称代名詞の種類別に感情が誰に生起しているか，誰に対しての感情であるかを判別することを目的とする．
139.	楊曄, Kazuyuki Matsumoto, 劉松, Fuji Ren and Shingo Kuroiwa : 語用情報に基づく『論語』の質問応答システムに関する研究, 言語処理学会第13回年次大会, 1010-1013, Mar. 2007. (Summary) 我々は，『論語』に関する単なる全文検索システムではなく，『論語』の内容検索を支援するシステムを開発している．本論文では，『論語』の言語表現の特徴に応じて，語用情報とカテゴリに基づく検索手法を提案した．抽出したカテゴリは，大分類と小分類がある．大分類は学習，教育,友，治国，教養など17種類ある．また，既存の『論語』関連サイトや著作を参考し，語用情報を抽出した．本システムを用いると，ユーザの質問に対し，『論語』からの適切な文章が返される． (Keyword) 論語 / 語用情報 / カテゴリ / 質問応答
140.	Kazuyuki Matsumoto, Fuji Ren and Shingo Kuroiwa : 語の意味情報を考慮した感情推定アルゴリズム, 言語処理学会第11回年次大会, Vol.S1-2, 145-148, Mar. 2005. (Summary) It is an indispensable point to recognize and extract the information in human sensitivities(sound, face expressions, and emotions) to actualize the communication without any sense ofincongruity with human. In our research group, though the research on the sensible computerconstruction has been carried out as an attempt to make the computer have the warmheartnessor kindness that are special to human, for the present, the research on the feeling estimationfrom the human conversation sentences is in practice as the fundamental research for theconstruction of welfare robot with sensitivities that support the lives of elders and reducethe caring burden. In this paper, we proposed the emotion estimation algorithm from theconversation sentences based on the attribute of word. (Keyword) conversation / welfare robots / natural language processing / sensibility
141.	Kazuyuki Matsumoto, Shingo Kuroiwa and Fuji Ren : About the emotion measurement system, IEICE Technical Report, Vol.103, No.114, 55-60, Jun. 2003. (Summary) It is necessary to nurse the senior citizen for declining population of children and increasing nuclear family. We are researching and developing a welfare robot which has a sensibility to support the life of the senior citizen and to reduce nursing. One of the basic researches on such welfare robots is to measure the sensibility from conversation sentences, In this paper, an algorithm for measuring the sensibility is presented, and a prototype system based on the proposed algorithm has been constructed. (Keyword) conversation / welfare robot / natural language processing
142.	Kazuyuki Matsumoto, Fuji Ren and Shingo Kuroiwa : 感性コンピュータの基礎研究ーその1:感性情報処理の現状，問題と展望ー, 平成14年度電気関連学会四国支部連合大会, 280, Oct. 2002. (Summary) 本研究では，人間の感性を理解するコンピュータ「感性コンピュータ」を実現するために何が必要か，そして，現時点での問題点について考察する．

Et cetera, Workshop:

1.	Kazuyuki Matsumoto : 相関性・異質性・相互作用を考慮したマルチモーダル特徴融合に基づく感情認識およびストレス・認知機能分析, 第5回大学発ベンチャー創出研究会, Aug. 2023. (Summary) 近年話題の生成AIをはじめとする言語や画像に関する人工知能技術の多くは，Webから大企業が収集・保有する大規模なマルチメディアデータに基づく学習が不可欠である．しかし，言語，音声，画像といったマルチモーダル情報はモーダル別に単独で学習されることが多く，モーダル間の相関性や異質性を考慮したものはほとんどない．この理由として，整理された学習用マルチモーダルデータベース構築の難しさがある．また，多くの研究ではユニモーダル情報から特徴量を抽出し，単純結合させたり，個別のタスクに適応させた分類モデルに各特徴量を入力した出力を統合する方法がとられている．これらの技術では，モーダル間の関連を十分に生かしきれているといえない．本発表では，従来のマルチモーダル感情認識技術について紹介し，それらの技術の課題と，モーダル間の相関性・異質性・相互作用を考慮することの重要性について述べ，その応用先としてストレス検出や認知機能分析への展望を述べる． (Keyword) マルチモーダル感情認識 / ストレス分析 / 認知機能分析
2.	Ryota Ohga, Kazuyuki Matsumoto, Minoru Yoshida and Kenji Kita : ニコニコデータセットを用いた視聴者の感情を考慮した動画推薦アルゴリズムの提案, IDRユーザフォーラム 2022, Dec. 2022. (Keyword) natural language processing / artificial intelligence / classification / deep learning
3.	Daiki Mori, Kazuyuki Matsumoto, 篠山学, Minoru Yoshida and Kenji Kita : 個性を反映した対話モデルにおける発話文の感情及び性格推定, IEICE Technical Report, 34-38, Oct. 2022. (Summary) One of the factors that enable humans to interact with AI in a more friendly manner is personality, and since the personalityrequired of AI varies depending on the situation and context, a mechanism is needed that allows AI to switch between different personalities depending on the person with whom he or she is interacting. In this study, we use a Japanese language Transformer Encoder-decoder dialogue model (japanese-dialog-transformers) and aim to construct a dialogue model that reflects the personality of the target person by fine-tuning the interview dialogue data. By analyzing the output of the proposed dialogue models using the MBTI estimation model and the sentiment analysis model, we evaluate the consistency of the personality and discuss the differences and similarities among the dialogue models. (Keyword) interview dialogue / personality / personality estimation / dialogue model / sentiment analysis
4.	NOKAMI Jumpei and Kazuyuki Matsumoto : コロナ禍におけるオンラインでの活動と新入生育成, 第17回大学教育カンファレンス in 徳島発表抄録集, 60-61, Jan. 2022. (Summary) 徳島大学ゲームクリエイトプロジェクトはゲーム開発を通じてプログラミング等の専門技術と社会人基礎力の向上を目的として活動している．目的達成のために大規模なゲームコンテストでの第一審査通過，またはノミネートを2021 年度の目標として掲げ，日々ゲーム開発を行っている． 2020 年度および2021 年度にかけて，新型コロナウイルス感染症拡大の影響により，本プロジェクトでは，オンラインをメインとした活動を行ってきた．本発表では，その成果と反省について述べる． (Keyword) online meeting / 新入生育成 / ゲームクリエイトプロジェクト / ゲーム作成
5.	Shohta Ishida and Kazuyuki Matsumoto : ゲーム開発プロジェクトの活動から得られたこと, 平成30年度全学FD推進プログラム大学教育カンファレンス in 徳島, 28-29, Dec. 2018.
6.	Shohta Yokoyama and Kazuyuki Matsumoto : ゲームクリエイトプロジェクトの活動を通じて得られたこと, 平成29年度全学FD推進プログラム大学教育カンファレンス in 徳島, 36-37, Jan. 2018.
7.	Kenji Kita, Kazuyuki Matsumoto, Minoru Yoshida, Satoru Tsuge, Norihide Kitaoka and 武田一哉 : ビット演算に基づく高速な音声ドキュメント検索語検出, 第8回音声ドキュメント処理ワークショップ, Mar. 2014.
8.	Hiroyuki Mitsuhara, Masami Shishibori, Hiroaki Ogata, Masao Fuketa, Hitoshi Tokushige, Kazuhiro Morita, Kazuyuki Matsumoto, Shun Watanabe and Momoyo Ito : 知能情報工学科``ソフトウェア設計及び実験''のエンタテインメント化の試行, 平成22年度全学FD大学教育カンファレンス in 徳島発表抄録集, 12-13, Jan. 2011.

Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)

専門家と非専門家の違いを考慮した科学的テキスト分析に関する研究 (Project/Area Number: 24K15193 )
Interview dialogue system for augmenting awareness opportunity (Project/Area Number: 22K12213 )
数値および専門性と話題性に着目したSNSからの科学的知識抽出に関する研究 (Project/Area Number: 21K12141 )
異なるコミュニティにおいて誤解されやすい表現の感性的言語変換に関する研究 (Project/Area Number: 20K12027 )
Research on Interview Dialogue to obtain User's good points by Conversation Robot (Project/Area Number: 19K12174 )
Analysis of lifestyle and health texts on the WWW consisting of text and numeric data. (Project/Area Number: 18K11549 )
Construction of a slang sensitivity dictionary by fusion of invariant information and variable information (Project/Area Number: 15K16077 )
Integrated Media Analysis for Medical/Health Information Matching on WWW (Project/Area Number: 15K00425 )
Creation of an evolutionary advanced intelligent robot (Project/Area Number: 15H01712 )
The research of emotion estimation method based on wording fluctuations (Project/Area Number: 23700252 )
To Create Robot Mind and Apply it to Nursing Robots (Project/Area Number: 22240021 )
Establishment of an academic system of Enriching Mental Engineering (Project/Area Number: 21650030 )
Semantic and Affective Multimedia Retrieval using EEG-based Biological Information (Project/Area Number: 21300036 )
Search by Researcher Number (90509754)

Associate Professor : Matsumoto, Kazuyuki

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Grants-in-Aid for Scientific Research (KAKEN Grants Database @ NII.ac.jp)