Tokushima University / Educator and Researcher Directory --- Yoshida, Minoru

Personal Web Page

https://researchmap.jp/mino8001 (researchmap)

Field of Study

○	Informatics [Intelligent informatics]

Subject of Study

Book / Paper

Book:

1.	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
2.	Minoru Yoshida and Kenji Kita : Mining Numbers in Text: A Survey, IntechOpen, Jul. 2021. (Link to Publication Site) ● Publication site (DOI): 10.5772/intechopen.98540 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.5772/intechopen.98540 (DOI: 10.5772/intechopen.98540)
3.	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)
4.	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)
5.	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)
6.	Minoru Yoshida, Torisawa Kentaro and Jun'ichi Tsujii : Extracting Attributes and Their Values from Web Pages, 2003. (Link to Publication Site) ● Publication site (DOI): 10.1142/9789812775375_0010 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1142/9789812775375_0010 (DOI: 10.1142/9789812775375_0010)

Academic Paper (Judged Full Paper):

1.	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(AMLAI), Vol.4, No.2, 32-40, 2023.
2.	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
3.	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)
4.	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)
5.	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)
6.	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)
7.	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)
8.	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): 1050573243107020800 ● Search Scopus @ Elsevier (DOI): 10.4018/IJITWE.298654 (Tokushima University Institutional Repository: 116756, DOI: 10.4018/IJITWE.298654, CiNii: 1050573243107020800)
9.	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)
10.	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)
11.	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): 1050570387105486080 (Tokushima University Institutional Repository: 116122, CiNii: 1050570387105486080)
12.	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)
13.	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)
14.	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)
15.	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): 1050845763178553728 (Tokushima University Institutional Repository: 113245, CiNii: 1050845763178553728)
16.	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)
17.	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)
18.	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)
19.	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)
20.	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): 1050564288205659776 (Tokushima University Institutional Repository: 113250, CiNii: 1050564288205659776)
21.	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): 1050845763182370176 (Tokushima University Institutional Repository: 113249, CiNii: 1050845763182370176)
22.	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): 1050282813228949248 ● Search Scopus @ Elsevier (DOI): 10.20469/ijtes.4.10002-1 (Tokushima University Institutional Repository: 113251, DOI: 10.20469/ijtes.4.10002-1, CiNii: 1050282813228949248)
23.	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): 1050001338252239488 (Tokushima University Institutional Repository: 113252, CiNii: 1050001338252239488)
24.	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)
25.	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)
26.	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): 1050282677900653056 (Tokushima University Institutional Repository: 113455, CiNii: 1050282677900653056)
27.	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)
28.	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)
29.	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): 1050001338855354112 (Tokushima University Institutional Repository: 113910, CiNii: 1050001338855354112)
30.	Fujisawa Akira, 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): 1050845763785485824 (Tokushima University Institutional Repository: 113909, CiNii: 1050845763785485824)
31.	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): 1050001338855352832 (Tokushima University Institutional Repository: 113906, CiNii: 1050001338855352832)
32.	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): 1050845763785484800 (Tokushima University Institutional Repository: 113907, CiNii: 1050845763785484800)
33.	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)
34.	藏本貴久, 和泉潔, Shinobu Yoshimura, 石田智也, 中嶋啓浩, 松井藤五郎, Minoru Yoshida and 中川裕志 : Analysis of Long-term Market Trend by Text-Mining of News Articles, Transactions of the Japanese Society for Artificial Intelligence, Vol.28, No.3, 291-296, 2012. (Summary) In this study, we developed a new method of the long-term market analysis by using text-mining of news articles. Using our method, we conducted extrapolation tests to predict stock price averages by 19 industry and two market averages, TOPIX and Nikkei225 for about 10 years. As a result, 8 sectors in 21 sectors (about 40%) showed over about 60% accuracy, and 15 sectors in 21 sectors (over 70%) showed over about 55% accuracy. We also developed a web system of financial text-mining based on our method for financial professionals. (Keyword) text-mining / stock prices prediction / market trend / UI (Link to Publication Site) ● Publication site (DOI): 10.1527/tjsai.28.291 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282680085059968 ● Search Scopus @ Elsevier (DOI): 10.1527/tjsai.28.291 (DOI: 10.1527/tjsai.28.291, CiNii: 1390282680085059968)
35.	Minoru Yoshida, 中川裕志 and 寺田昭 : Dynamic Synonym Candidates Extraction for Searching Documents in a Corpus, Transactions of the Japanese Society for Artificial Intelligence, Vol.25, No.1, 122-132, 2010. (Summary) This paper proposes a method for implementing real-time synonym search systems. Our final aim is to provide users with an interface with which they can query the system for any length strings and the system returns a list of synonyms of the input string. We propose an efficient algorithm for this operation. The strategy involves indexing documents by suffix arrays and finding adjacent strings of the query by dynamically retrieving its contexts (i.e., strings around the query). The extracted contexts are in turn sent to the suffix arrays to retrieve the strings around the contexts, which are likely to contain the synonyms of the query string. (Keyword) synonyms / suffix arrays / query support systems (Link to Publication Site) ● Publication site (DOI): 10.1527/tjsai.25.122 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390282680084681600 ● Search Scopus @ Elsevier (DOI): 10.1527/tjsai.25.122 (DOI: 10.1527/tjsai.25.122, CiNii: 1390282680084681600)
36.	Minoru Yoshida, 池田雅紀, 小野真吾, 佐藤一誠 and 中川裕志 : 二段階クラスタリングを単語重み付与に応用した人名曖昧性解消, 日本データベース学会論文誌, Vol.9, No.2, 19-24, 2010. (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1520572359771738496 (CiNii: 1520572359771738496)
37.	寺田昭, Minoru Yoshida and 中川裕志 : 同義語辞書作成支援ツール, Journal of Natural Language Processing, Vol.15, No.2, 39-58, 2008.
38.	Minoru Yoshida, Kentaro Torisawa and Jun'ichi Tsujii : Integrating Tables on the World Wide Web, Transactions of the Japanese Society for Artificial Intelligence, Vol.19, No.6, 548-560, 2004. (Keyword) HTML tables / EM algorithm / clustering (Link to Publication Site) ● Publication site (DOI): 10.1527/tjsai.19.548 (Link to Search Site for Scientific Articles) ● CiNii @ National Institute of Informatics (CRID): 1390001205109189504 ● Search Scopus @ Elsevier (DOI): 10.1527/tjsai.19.548 (DOI: 10.1527/tjsai.19.548, CiNii: 1390001205109189504)

Academic Letter:

1.	Minoru Yoshida, Kazuyuki Matsumoto and Kenji Kita : Value行列を手掛かりとした Transformerの分析, Transactions of the Japanese Society for Artificial Intelligence, Vol.38, No.2, 2023. (Link to Publication Site) ● Publication site (DOI): 10.1527/tjsai.38-2_C-MB7 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.1527/tjsai.38-2_C-MB7 (DOI: 10.1527/tjsai.38-2_C-MB7)

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.	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, Proeedings of the19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery, 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
2.	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
3.	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
4.	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)
5.	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)
6.	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)
7.	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)
8.	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)
9.	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)
10.	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)
11.	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
12.	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
13.	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
14.	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)
15.	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)
16.	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)
17.	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)
18.	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, Oct. 2020.
19.	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
20.	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
21.	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)
22.	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.
23.	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)
24.	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)
25.	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)
26.	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
27.	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.
28.	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.
29.	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)
30.	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)
31.	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)
32.	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
33.	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)
34.	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)
35.	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
36.	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)
37.	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)
38.	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
39.	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
40.	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
41.	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
42.	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
43.	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
44.	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
45.	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
46.	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
47.	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
48.	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
49.	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)
50.	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.
51.	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.
52.	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
53.	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)
54.	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)
55.	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)
56.	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)
57.	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.
58.	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
59.	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
60.	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.
61.	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)
62.	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)
63.	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)
64.	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)
65.	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
66.	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)
67.	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)
68.	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
69.	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.
70.	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.
71.	Minoru Yoshida, Sato Issei, Nakagawa Hiroshi and Terada Akira : Mining Numbers in Text Using Suffix Arrays and Clustering Based on Dirichlet Process Mixture Models, Proceedings of PAKDD-2010 (The 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining), Springer (LNAI 6119), 230-237, 2010. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-642-13672-6_23 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-79956306011 (DOI: 10.1007/978-3-642-13672-6_23, Elsevier: Scopus)
72.	Minoru Yoshida, Ikeda Masaki, Ono Shingo, Sato Issei and Nakagawa Hiroshi : Person Name Disambiguation by Bootstrapping, Proceedings of SIGIR-2010 (the 33rd Annual ACM SIGIR Conference), 10-17, 2010. (Link to Publication Site) ● Publication site (DOI): 10.1145/1835449.1835454 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-77956022829 (DOI: 10.1145/1835449.1835454, Elsevier: Scopus)
73.	Minoru Yoshida, Nakagawa Hiroshi and Terada Akira : Gram-Free Synonym Extraction via Suffix Arrays, Proceedings of AIRS 2008 (the Fourth Asia Information Retrieval Symposium), Springer (LNCS 4993), 282-291, 2008. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-540-68636-1_27 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-45449093573 (DOI: 10.1007/978-3-540-68636-1_27, Elsevier: Scopus)
74.	Ono Shingo, Sato Issei, Minoru Yoshida and Nakagawa Hiroshi : Person Name Disambiguation in Web Pages using Social Network, Compound Words and Latent Topics, Proceedings of PAKDD2008 (The 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining), Springer (LNAI 5012), 260-271, 2008. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-540-68125-0_24 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-44649129207 (DOI: 10.1007/978-3-540-68125-0_24, Elsevier: Scopus)
75.	Sato Issei, Minoru Yoshida and Nakagawa Hiroshi : Knowledge Discovery of Semantic Relationships between Words Using Nonparametric Bayesian Graph Model, Proceedings of KDD-2008 (Fourteenth International Conference on Knowledge Discovery and Data Mining), ACM, 587-595, 2008. (Link to Publication Site) ● Publication site (DOI): 10.1145/1401890.1401962 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-65449127481 (DOI: 10.1145/1401890.1401962, Elsevier: Scopus)
76.	Minoru Yoshida and Nakagawa Hiroshi : Web Document Parsing: A New Approach to Modeling Layout-Language Relations, Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR2007), IEEE Computer Society, 203-207, 2007. (Link to Publication Site) ● Publication site (DOI): 10.1109/ICDAR.2007.4378704 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-51149114613 (DOI: 10.1109/ICDAR.2007.4378704, Elsevier: Scopus)
77.	Ono Shingo, Minoru Yoshida and Nakagawa Hiroshi : NAYOSE: A System for Reference Disambiguation of Proper Nouns Appearing on Web Pages, Proceedings of AIRS2006 (Asia Information Retrieval Symposium), Springer (LNCS 4182), 338-349, 2006. (Link to Publication Site) ● Publication site (DOI): 10.1007/11880592_26 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-33751369671 (DOI: 10.1007/11880592_26, Elsevier: Scopus)
78.	Minoru Yoshida and Nakagawa Hiroshi : Reformatting Web Documents via Header Trees, Proceedings of the 43rd ACL2005 Poster/Demo Session, 121-124, 2005. (Link to Publication Site) ● Publication site (DOI): 10.3115/1225753.1225784 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-84859886917 (DOI: 10.3115/1225753.1225784, Elsevier: Scopus)
79.	Minoru Yoshida and Nakagawa Hiroshi : Automatic Term Extraction based on Perplexity of Compound Words, Proceedings of IJCNLP 2005, Springer (LNAI 3651), 269-279, 2005. (Link to Publication Site) ● Publication site (DOI): 10.1007/11562214_24 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-33645958849 (DOI: 10.1007/11562214_24, Elsevier: Scopus)
80.	Minoru Yoshida and Nakagawa Hiroshi : Specification Retrieval -- How to Find Attribute-Value Information on the Web, Proceedings of ijcNLP (International Joint Conference of Natural Language Processing),(Springer LNAI 3248), 338-347, 2004. (Link to Publication Site) ● Publication site (DOI): 10.1007/978-3-540-30211-7_36 (Link to Search Site for Scientific Articles) ● Summary page in Scopus @ Elsevier: 2-s2.0-26444463338 (DOI: 10.1007/978-3-540-30211-7_36, Elsevier: Scopus)
81.	Minoru Yoshida : Extracting Attributes and Their Values from Web Pages, Proceedings of the ACL-02 Student Research Workshop, 72-77, 2002.
82.	Minoru Yoshida, Torisawa Kentaro and Tsujii Jun'ichi : Extracting ontologies from World Wide Web via HTML tables, Proceedings of the Pacific Association for Computational Linguistics (PACLING 2001), 332-341, 2001.
83.	Minoru Yoshida, Torisawa Kentaro and Jun'ichi Tsujii : A method to integrate tables of the World Wide Web, Proceedings of the International Workshop on Web Document Analysis (WDA 2001), 31-34, 2001.
84.	Minoru Yoshida, Ninomiya Takashi, Torisawa Kentaro, Makino Takaki and Jun'ichi Tsujii : Efficient FB-LTAG Parser and its Parallelization, Proceedings of PACLING'99, 90-103, 1999.
85.	Makino Takaki, Minoru Yoshida, Torisawa Kentaro and Jun'ichi Tsujii : LiLFeS -- Towards Practical HPSG Parsers, Proceedings of the 17th International Conference on Conmupational Linguistics and the 36th Annua Meeting of the Association for Computational Linguistics, (COLING-ACL 98), 807-811, 1998. (Link to Publication Site) ● Publication site (DOI): 10.3115/980691.980702 (Link to Search Site for Scientific Articles) ● Search Scopus @ Elsevier (DOI): 10.3115/980691.980702 (DOI: 10.3115/980691.980702)

Proceeding of Domestic Conference:

1.	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
2.	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
3.	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
4.	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
5.	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
6.	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
7.	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
8.	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
9.	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
10.	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
11.	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
12.	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
13.	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
14.	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.
15.	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
16.	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
17.	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
18.	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
19.	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
20.	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
21.	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
22.	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
23.	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
24.	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
25.	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.
26.	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
27.	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講演論文集, 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.
28.	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.
29.	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
30.	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
31.	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.
32.	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.
33.	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
34.	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) 深層ニューラルネットワーク / クロスメディア検索
35.	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
36.	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.
37.	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.
38.	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
39.	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
40.	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
41.	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
42.	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
43.	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
44.	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
45.	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．
46.	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
47.	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
48.	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
49.	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
50.	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
51.	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
52.	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．
53.	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
54.	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
55.	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
56.	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
57.	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.
58.	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.
59.	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
60.	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
61.	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．
62.	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.
63.	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.
64.	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
65.	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.
66.	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.
67.	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.
68.	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.
69.	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
70.	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
71.	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
72.	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
73.	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
74.	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.
75.	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
76.	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.
77.	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
78.	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
79.	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
80.	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
81.	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
82.	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
83.	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
84.	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
85.	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
86.	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
87.	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

Et cetera, Workshop:

1.	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
2.	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
3.	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.

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 )
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 )
Analysis of lifestyle and health texts on the WWW consisting of text and numeric data. (Project/Area Number: 18K11549 )
A study on simultaneous extraction of SNS communities and terms (Project/Area Number: 15K00309 )
Integrated Media Analysis for Medical/Health Information Matching on WWW (Project/Area Number: 15K00425 )
Mining Numbers in Text for Various Kinds of Text Data (Project/Area Number: 24500162 )
Knowledge Discovery from Numbers in Text (Project/Area Number: 22700137 )
Web上のテキスト情報の信頼性と有益性の評価システムに関する研究 (Project/Area Number: 21013011 )
A STUDY OF RESOLVING LONG TAIL PHENOMENA BY MACHINE LEARNING (Project/Area Number: 21240011 )
多言語Webテキストからの知識マイニング関する研究 (Project/Area Number: 19024014 )
多言語Webテキストからの知識マイニングに関する研究 (Project/Area Number: 18049011 )
HTML文書からの輪理構造自動推定に関する研究 (Project/Area Number: 17700137 )
A Study for Knowledge ExtractionAid System from Web Text (Project/Area Number: 17200007 )
分かり易さ向上のためのテキストコンテンツ変換に関する研究 (Project/Area Number: 16016215 )
分かり易さ向上のためのテキストコンテンツ変換に関する研究 (Project/Area Number: 15017217 )
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Associate Professor : Yoshida, Minoru

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