Predicting Physical and Mental Health Status One Month Later through Interview-Based Stress Assessment

This study conducted an interview-based stress evaluation that considered the psychosocial models of work stress and verified the evaluations predictive validity. A four-stage assessment comprising a pre-survey, pre-interview questionnaire, stress assessment interview, and post-survey after one month was conducted with 50 Japanese workers. Additionally, 16 occupational health professionals provided stress evaluations based on recorded interview videos. Variables based on intraclass correlation coefficients (ICCs) were computed in multiple ways to compare the agreement among the evaluators. The generalized estimating equation (GEE) was conducted to evaluate the prediction models. The overall ICC among the evaluators was 0.58. The GEE revealed that the mean score of the evaluators in the interview-based stress evaluation significantly predicted psychological symptoms (β =2.02, p=0.019), burnout (β =0.77, p<0.001), and well-being (β =0.64, p=0.007) one month later, even after adjusting for the self-reported stress levels measured in the pre-survey. The predictive validity of the proposed interview-based stress evaluation was confirmed. Although there are several challenges in standardizing this evaluation, semi-structured interviews are an effective tool for understanding work stress.


An Exploratory Study of the Potential of Online Counseling for University Students by a Human-Operated Avatar Counselor

Recently, the use of digital technologies, such as avatars and virtual reality, has been increasingly explored to address university students mental health issues. However, there is limited research on the advantages and disadvantages of counselors using avatars in online video counseling. Herein, 25 university students were enrolled in a pilot online counseling session with a human counselor-controlled avatar, and asked about their emotional experiences and impressions of the avatar and to provide qualitative feedback on their communication experience. Positive emotions during the session were associated with impressions of the avatars intelligence and likeability. The anthropomorphism, animacy, likeability, and intelligent impressions of the avatar were interrelated, indicating that the avatars smile and the counselors expertise in empathy and approval may have contributed to these impressions. However, no associations were observed between participant experiences and their prior communication with avatars, or between participant experiences and their gender or the perceived gender of the avatar. Accordingly, recommendations for future practice and research are provided. Accumulating practical and empirical findings on the effectiveness of human-operated avatar counselors is crucial for addressing university students mental health issues.


Causal Effects of High Stress Assessed via Interviews on Mental and Physical Health: Toward Computer Agent-Driven Stress Assessment

This study investigated the causal effect of high stress assessment via an interview on the mental and physical health of workers one month later. Stress assessment interviews and feedback were conducted with 50 Japanese workers. In addition to the interviewer, two occupational health professionals assessed participants' stress based on recordings. The average treatment effect was estimated by propensity score matching. High stress, according to the interview-based assessment, had a significant negative causal effect on self-reported well-being one month later (95% confidence interval: -3.02, -1.10). In addition, no effect of high stress on stress load, mental and physical symptoms, or burnout was observed. This study provides important insights into the prognosis of individuals who were assessed through interviews to have high stress. The findings are expected to help automate stress assessments using computer agents.

Journal of Occupational and Environmental Medicine


Tracking Emotions using an Evolutionary Model of Mental State Transitions: Introducing a New Paradigm


Using Machine Learning to Classify Information Related to Child Rearing of Infants from Twitter

Advances in Machine Learning & Artificial Intelligence


System to Correct Toxic Expression with BERT and to Determine the Effect of the Attention Value

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.

Communications in Computer and Information Science


Determination of individual factors associated with hallux valgus using SVM-RFE

Introduction This cross-sectional study aimed to determine the factors related to hallux valgus (HV) and their importance using support vector machine-recursive feature elimination (SVM-RFE). Methods A total of 864 participants aged 18 years were enrolled. The Manchester scale was used to determine the presence of HV (summed scores for both feet 4). The questionnaire included items such as age, sex, height, weight, and foot measurements. These internal factors were analyzed to determine if they are related to HV using SVM-RFE. Results The results of tenfold cross-validation using SVM-RFE revealed that the numbers of feature selections were 10, 10, and 9 for age, sex, and body weight, respectively, and these factors were shown to be related to HV. HV was found to be more common in women than in men (women, 24.9%; men, 7.6%), but the sex difference was not significant in older people. Conclusion Age and sex were found to be important factors associated with HV identified via feature selection using SVM-RFE.


Comparison of Subjective Facial Emotion Recognition and Facial Emotion Recognition Based on Multi-Task Cascaded Convolutional Network Face Detection between Patients with Schizophrenia and Healthy Participants


Trend Prediction Based on Multi-Modal Affective Analysis from Social Networking Posts

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.

本論文では，SNS上の投稿テキストや添付画像などのマルチモーダル情報を対象に，投稿内容から感情情報を分析し，バズや流行の発生のレベルを予測する手法を提案する．提案手法は，事前予測に用いる場合は投稿時の情報のみを用い，事後分析に用いる場合は時間誤差の情報を用いて，様々な角度から拡散スケールを分析することが可能である．具体的には，事前に学習させた汎用言語モデルBERTを用いてツイートと返信ツイートをベクトル化し，添付画像を学習させた画像認識用ニューラルネットワークモデルを用いて特徴ベクトルに変換する．また，投稿内容の感情情報を解析するために，独自の感情解析モデルを用いて，ツイート，返信ツイート，画像特徴から感情を推定し，感情特徴として入力に追加する．評価実験の結果，ツイートに言語特徴(BERTベクトル)と画像特徴を付加した提案手法は，単一特徴のみを用いた手法と比較して高い性能を達成することができた．感情特徴量の効果を明確に観測することはできなかったが，考察及び分析の結果，ツイートとその返信の一致する感情が多いほど，共感行動が発生し，いいねやRTの値が大きくなる傾向があり，結果的にツイートがバイラル化する可能性が高くなると考えられる．


Extraction of Lifestyle Habits Described in Blogs of Diabetics

糖尿病患者のブログに記述された生活習慣の抽出

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.

本研究は難病患者の闘病活動における生活習慣の維持や管理を支援するシステムを構築するための一歩として，糖尿病患者の生活習慣(食事，睡眠，運動など)や病状(血圧，体調など)に関連したキーワードの抽出を目指す．糖尿病の闘病患者のブログデータを収集し，そこから双方向ゲート付きリカレントユニット(GRU)を用いて生活習慣や病状に関連したキーワードの自動抽出を行う．

IEEJ Transactions on Electronics, Information and Systems

電気学会論文誌C (電子，情報，システム部門誌)


Emotional Similarity Word Embedding Model for Sentiment Analysis

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.


Investigation of Methods to Create Future Multimodal Emotional Data for Robot Interactions in Patients with Schizophrenia: A Case Study


Characteristics of interactive communication between Pepper robot, patients with schizophrenia, and healthy persons


Emotion Analysis and Dialogue Breakdown Detection in Dialogue of Chat Systems Based on Deep Neural Networks

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.


Emotion Estimation Method Based on Emoticon Image Features and Distributed Representations of Sentences

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.


Topic Break Detection in Interview Dialogues Using Sentence Embedding of Utterance and Speech Intention Based on Multitask Neural Networks

Currently, task-oriented dialogue systems that perform specific tasks based on dialogue are widely used. Moreover, research and development of non-task-oriented dialogue systems are also actively conducted. One of the problems with these systems is that it is difficult to switch topics naturally. In this study, we focus on interview dialogue systems. In an interview dialogue, the dialogue system can take the initiative as an interviewer. The main task of an interview dialogue system is to obtain information about the interviewee via dialogue and to assist this individual in understanding his or her personality and strengths. In order to accomplish this task, the system needs to be flexible and appropriate for detecting topic switching and topic breaks. Given that topic switching tends to be more ambiguous in interview dialogues than in task-oriented dialogues, existing topic modeling methods that determine topic breaks based only on relationships and similarities between words are likely to fail. In this study, we propose a method for detecting topic breaks in dialogue to achieve flexible topic switching in interview dialogue systems. The proposed method is based on multi-task learning neural network that uses embedded representations of sentences to understand the context of the text and utilizes the intention of an utterance as a feature. In multi-task learning, not only topic breaks but also the intention associated with the utterance and the speaker are targets of prediction. The results of our evaluation experiments show that using utterance intentions as features improves the accuracy of topic separation estimation compared to the baseline model.


Relationship Between Personality Patterns and Harmfulness: Analysis and Prediction Based on Sentence Embedding

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.

International Journal of Information Technology and Web Engineering


Construction and Expansion of Dictionary of Idiomatic Emotional Expressions and Idiomatic Emotional Expression Corpus

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.

International Journal of Computer & Software Engineering


Buzz Tweet Classification Based on Text and Image Features of Tweets Using Multi-Task Learning

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


Development Issues of Healthcare Robots: Compassionate Communication for Older Adults with Dementia

International Journal of Environmental Research and Public Health


Human Psychological Burden and Thinking Process while Operating a Humanoid Robot Conversation App Program

International Journal of Advanced Intelligence (IJAI)


Practical issues in Developing Natural Language Processing Applications for Healthcare Robot Motivation of Older Persons Physical Rehabilitation


The AI Development Through Transforming Tacit Knowledge to Explicit Knowledge of Nurses' Dialogue for Patients with Dementia


Developmental Issues of Communication for Robot Applications in Older People Care: An Integrative Review


Toward Analyzing Relations between Sleep Time and Social Networking Service Texts: Prediction of the Tweet Time Span Using the Last Tweet of the Day

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.


Relating Moris Uncanny Valley in generating conversations with artificial affective communication and natural language processing


Data Augmentation for Lyrics Emotion Estimation

Lyrics emotion estimation can allow us to realise song retrieval systems and song recommendationsystems which are based on not only text retrieval nor melody matching but also emotions in lyricsor transitions of emotions within lyrics of a whole song. This requires lyrics emotion corpora ofphrase. However, it is difficult to build large scale lyrics emotion corpora because emotions arelabelled manually. In this paper, we propose a method to augment lyrics emotion corpora. As a result,we augmented a corpus consisting of 366 phrases into a larger corpus consisting of 2145 phrases. Wealso evaluate the proposed method using 2 convolutional neural networks trained on original corpusand augmented corpus respectively. We define the target emotion classes as Joy, Love, Anger, Sorrowand Anxiety. Mean accuracy of the model trained on the augmented corpus was 75.9% whilst themodel trained on the original corpus performed 70.7%.


Estimating Psychological Impressions from Color Images Using Earth Mover's Distance

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.

本論文では，配色イメージスケールおよび言語イメージスケールを元に，画像イメージを簡便に評価できるシステムについて述べた．また，そのシステムによる評価結果と，人間の感性による評価結果の一致率を検討し，イメージスケール上のカテゴリーの再検討を行った．

Transactions of Japan Society of Kansei Engineering


Classification of Smartphone Application Reviews Using Small Corpus Based on Bidirectional LSTM Transformer

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).

International Journal of Machine Learning and Computing


Factor Analysis of Individual Factors Associated with Low Arch Height Ratio of the Foot by SVM-RFE

SVM-RFEを用いた足部アーチ高比の低下を有する人の関連要因分析

Transactions of the Japanese Society for Medical and Biological Engineering


健常者の足背高測定による足部アーチ評価とアーチ高に影響を及ぼす因子

健常者の足背高測定による足部アーチ評価とアーチ高に影響を及ぼす因子


Twitter User's Hobby Estimation Based on Sequential Statements Using Deep Neural Networks

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.

近年，SNS上でのユーザー同士のコミュニケーションが増大している．TwitterやFaacebookを始める際，多くのユーザーが，同じ趣味の友達を探す．本研究では，Twitter上のツイートに基づいてユーザの興味(趣味)を推定する手法を提案する． 1つのツイートは，多くの情報を含んでいるわけではなく，また，ツイートの中にはユーザーの趣味に関係していないものもある．そこで，複数の連続したつぶやきから特徴を抽出し，信頼できる趣味推定法を提案する．提案手法では，時系列情報に対応できるリカレントニューラルネットワーク(RNN)を用いる．また，文脈情報を扱うことができる畳み込みニューラルネットワーク(CNN)も使用する．特徴量としては，単語分散表現の平均ベクトルを使用した．Long Short-Term Memory RNN (LSTM-RNN)に基づく提案された方法を使用して，機械学習アルゴリズムとしてランダムフォレスト(RF)回帰を使用するベースライン方法と比較して23.72%の改善を得た．


Emotion Recognition for Japanese Short Sentences Including Slangs Based on Bag of Concepts Feature Trained by Large Web Text

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.

Current Analysis on Instrumentation and Control


Slang Feature Extraction by Analyzing Topic Change on Social Media

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.

CAAI Transactions on Intelligence Technology


ASCII Art Classification based on Deep Neural Networks Using Image Feature of Characters

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.


Similarity Analysis and Repeating Pattern Detection in Fingerprint Features

Journal of Computational Methods in Sciences and Engineering


Intimacy Estimation of the Characters in Drama Scenario

演劇台本における登場人物間の親密度推定手法

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.

近年，音声アシスタント機能を搭載した携帯型端末が普及し，より使い手に配慮した対話システムが求められている． 従来型の対話システムの問題点として，雑談のような非タスク型対話への対応が未熟な点があげられる．非タスク型対話 においては，ユーザとの会話を円滑かつ柔軟にするための工夫が必要となる．たとえば，ユーザの現実世界での人間関係 を考慮することによって，ユーザと親しい人物に関する話題の提供を積極的に行ったり，ユーザと親しくない(仲が良く ない)人物に関する話題の提供を避けたりすることができると考える．本論文では，演劇台本を題材に，対話中の2 者間 の人間関係を「親密度」という尺度により表現する．親密度に関わると考えられる要素として，発話の応答回数や発話中 の態度などがある．本論文では，その中でも発話中の感情状態に着目することで，高精度な親密度推定の実現を試みる． 評価実験の結果，発話役割に基づく従来手法を上回る高精度な親密度推定を実現することが出来た．

Journal of Japan Society for Fuzzy Theory and Intelligent Informatics


Emotion Estimation Adapted to Gender of User Based on Deep Neural Networks

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.

International Journal of Advanced Intelligence (IJAI)


Index Generation of BGM Video Based on Distinctive Comments

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.


Flame Prediction Based on Harmful Expression Judgement Using Distributed Representation

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.

近年，ソーシャルメディア上での炎上が問題になっている．炎上を避けるためには，投稿前に自動的にメッセージを確認して，炎上を引き起こす可能性の高い表現が含まれているかどうかを判断することが役立つ．提案手法では，有害表現辞書を作成する．このさい，表現を集めるコストを最小限に抑えるために，単語分散表現を使用して半自動的に辞書を構築した．辞書に登録された有害表現と一般的な表現を用い，有害表現と一般的表現の分類を行った．評価実験により，提案手法は約70%の精度で有害表現を抽出できることが分かった．提案手法は未知の表現を抽出することもできたが，有害でない表現も有害として抽出する傾向があった．提案手法は，基本辞書に含まれていない未知の有害な表現を決定することができ，有害表現間の意味的関係を判定することができる．

International Journal of Technology and Engineering Studies


In this paper, we describe a method of emotion analysis on social big data. Social big data means text data that is emerging on In ternet social networking services.We collect multilingual web corpora and annotated emotion tags to these corpora for the purpose of emotion analysis. Because these data are constructed by manual annotation, their quality is high but their quantity is low. If we create an emotion analysis model based on this corpus with high quality and use the model for the analysis of social big data, we might be able to statistically analyze emotional sensesand behavior of the people in Internet communications, which we could not know before. In this paper, we create an emotion analysis model that integrate the high quality emotion corpus and the automatic constructed corpus that we created in our past studies, and then analyze a large scale corpus consisting of Twitter tweets based on the model. As the result of time series analysis on the large scale corpus and the result of model evaluation, we show the effective ness of our proposed method.

本稿では，社会的ビッグデータに対する感情分析手法について述べる．ソーシャルビッグデータとは，インターネットソーシャルネットワーキングサービスに出現しているテキストデータを意味します．私たちは，感情分析のために，これらのコーパスに多言語Webコーパスと注釈付き感情タグを収集します．これらのデータは手作業による注釈で構成されているため，品質は高いが量は少ない．このコーパスに基づいて高品質の感情分析モデルを作成し，社会的ビッグデータの分析に用いると，以前には知り得なかったインターネット通信における感情的感情や人の行動を統計的に分析することができます．本稿では，過去の研究で作成した高性能感情コーパスと自動構築体を統合し，そのモデルに基づいたTwitterのツイートからなる大規模なコーパスを解析するための感情分析モデルを作成する．大規模尺度コーパスの時間軸解析とモデル評価の結果，提案手法の有効性を示す．


Review Score Estimation Based on Transfer Learning of Different Media Review Data

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.

本研究では，異なるメディアのレビューデータに基づいたレビュー分類モデルの構築を提案します．現在，多種多様なビッグデータを対象とした異なるドメイン間での転移学習に関する研究が盛んである．異なるドメイン間では，評価表現が異なることが多く，評判分析の障壁となる．異なるメディアにおける著作物に触れているユーザが異なる表現を使用することは多い．たとえば，著作物に関して，「アニメ」や「漫画」，「ゲーム」，「映画」など，異なるメディアにおいては異なる用語や表現が存在する．評価表現以外にもメディアの違いの特徴が表れると考えられる．我々はメディア間でのレビューデータの転移学習をおこなうことで，こうした違いが分類精度にどんな影響を及ぼすかについて分析した．本研究では，著作物の媒体ごとに，転移先と転移元のメディアの相性について，レビューの評価分類モデルを構築することで明らかにした．また，評価実験の結果，Long Short Term Memoryを用いて，レビュー片の学習においてSo-Scoreを用いずに，SO-Scoreに基づく手法よりも正確に評価スコアの推定をおこなえた．


Emotion Recognition of Emoticon Based on Character Embedding

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.

本研究では，顔文字の文字意味分散表現を用いた顔文字の感情認識をおこなう．従来の顔文字研究では，文中から顔文字を抽出したり，顔文字を構成パーツに分解しその組み合わせによる感情認識などがおこなわれてきた．近年発展してきた単語埋め込み技術を応用し，顔文字の文字単位での意味を学習し，特徴量として用いることで，より汎用的な顔文字の感情認識を試みる．文字の分散表現を素性として深層畳み込みニューラルネットワークに学習させる手法により構築した顔文字感情認識モデルを交差検証法により評価した結果，未知顔文字に対する感情認識を，単純な文字n-gram出現頻度ベクトルを素性として用いた手法よりも高い精度でおこなえることが分かった．


Constructing A Short Text Conversation system Based on the Relations Between Posts and Comments


An Improvement of Pedestrian Detection Method with Multiple Resolutions

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.


Ego-state Estimation from Short Texts Based on Sentence Distributed Representation

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.

人間の性格は，複雑な要素が組み合わさって，多面的に構成される．性格診断手法としてのエゴグラムは，5つの自我状態の高低の組み合わせに基づき，性格をパターン分類するものである．近年のSNSの発達にともなって，性格をSNS上の発言から診断しようという試みが増えてきた．しかし，発言の表層情報を手掛かりとした性格診断は，自我状態を推定するうえでいくつかの問題を抱える．具体的には，性格がすべての発言に反映されているわけではないこと，また，個人の性格に起因する発言の傾向は時間とともに変化していくものであるということなどがあげられる．また，十分な量の性格診断結果を伴う発言の事例を収集することも重要となる．本研究では，SNS，とくにMicroblog 上の発言(ショートテキスト)をもとに，エゴグラム診断を自動化するために，Twitter 上の発言を事前学習において分散表現(文ベクトル)により表し，Deep Neural Network による機械学習を用いて，ユーザごとに自我状態のレベルを推定するモデルの構築を試みる．評価実験の結果，提案手法による自我状態推定モデルは，Bag of Words を素性としたベースライン手法よりも高精度%であることを示した．また，時間経過による性格変化を調べるため，エゴグラム診断の前後において，推定結果の一致率にどのような変化が起きているかを分析した．さらに本論文では，文のフォーマル度合いを特徴づける素性を追加することで性格パターン分類の精度向上を確認した．


Conversion of Japanese Slang into Standard Japanese Considering Sensibility

感性を考慮した日本語俗語の標準語変換

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.

近年のソーシャルメディアを用いたコミュニケーションの普及に伴い，Web上での意見交換が老若男女問わず一般的になってきた．一方で，「ネット炎上」と呼ばれる問題が，ソーシャルネットワークサービスのユーザの増加にともない，しばしば起こっている．この理由は，ユーザが単語により表現された意味/意図/感情を理解しないためである．本研究では，SNS上でよく用いられるが，一般的な辞書に登録されていない俗語(ネットスラング)に着目し，それらを標準語に変換することを試みる．我々は，意味的な類似だけでなく，感性的な類似を考慮し，より適切な候補を出力しようと考える．提案手法は，分散表現に基づき得られた候補を，2つの点に焦点をあて，フィルタリングし再ランキングする．入力語と出力候補間の，(1)俗語/標準語の特徴，(2)感性的類似度 の2点である．評価実験において，提案手法により，ベースライン手法よりも高いMRRを得た．

Transactions of the Japanese Society for Artificial Intelligence


Reidentification of Persons Using Clothing Features in Real-Life Video

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.

Applied Computational Intelligence and Soft Computing


Slang Analysis Based on Variant Information Extraction Focusing on the Time Series Topics

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.

近年，SNS の利用者増加に伴い，Web上でのコミュニケーションがよりいっそう活発になった． これにより，言葉の多様性について，SNS上のビッグデータを用いて解析できる可能性が出てきた．日本語では扱われる文字種が多いことからも，様々な表現が存在し，自然言語処理において基本的な処理である形態素解析が，比較的難しいことが問題となっている．こうした形態素解析の辞書に登録されていないような未知表現に対しては，厳密な定義が存在していないこともしばしばであり，意味解釈における個人差が大きいことが考えられる．本研究では，Twitter上の俗語に関連したトピックを時系列で分析する．本論文では，検証実験として，連続するTweetデータを用いたトピックの時系列分析実験をおこない，俗語の種類によるトピック変化の違いを考察する．変化しない情報(不変情報)が，一定期間においてどのような変化を示すかを実験により示し，標準語との差異を調べる．また，俗語の変化の仕方を変動値とし，特徴ベクトルとすることで，似たような傾向を示す俗語をクラスタリングする手法を提案し，実験結果を分析する．

International Journal of Advanced Intelligence (IJAI)


An Illustration Image Classification Focusing on Infrequent Colors

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.

漫画やアニメ作品において用いられるイラスト画像は感性的な特徴である作風を持つ．作風はイラスト画像の様々な要素により表現される．イラスト画像の作風を扱った研究は少ない．本論文では，画像特徴量を用いることで作風のモデル化およびイラスト画像の作風による分類を試みた．我々は，イラスト画像においてあまり使用されない色が作風を表現していると考えた．そのため，あまり出現しない色を強調した色ヒストグラムを作成するための手法を提案する．我々はこのヒストグラムを「Infrequency histogram(IF-hist)」と呼ぶ．このヒストグラムの有効性を確認するため，我々は，IF-histを用いて「少年向け」と「少女向け」の2つの作風に分類する実験をおこなった．実験の結果，通常の色ヒストグラムを用いた場合よりも50%の精度改善を確認でき，本手法の有効性を示すことができた．


Retrieving Vaguely Remembered Lyrics Using N-Gram Edit Distance


Pedestrian Re-identification using Color Feature in Multi Surveillance Video

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.

International Journal of Advances in Electronics and Computer Science


A Study on the Review Analysis for the Automatic Evaluation of the Product

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.

International Journal of Management and Applied Science (IJMAS)


Semi-automatic Creation of Youth Slang Corpus and Its Application to Affective Computing

This paper proposes a method to semi-automatically construct a corpus that includes Japanese youth slang called Wakamono Kotoba. The process of semi-automatic corpus construction is composed of the first step is automatic collection of example sentence, the second step is tag annotation to collected sentences, and the final step is manually modifying tag and noise reduction. In this process, there are two problems. The first problem is quality of the automatic collected corpora. The second is the accuracy of tag annotation. If the automatically annotated tags are unreliable, after all, it takes long time to modify them manually. As a solution of the first problem, we proposed a filtering method to remove meaningless sentences (noise sentences) automatically. In order to solve a second problem, we proposed an emotion estimation method that can be applied to the sentences that included youth slang and were difficult to be analyzed automatically. The result of the accuracy evaluation showed improvement in F1-Score compared to the machine learning method and confirms the effectiveness of the proposed method.

本論文では，若者言葉を含んだ発話文のコーパスを半自動構築するための手法を提案する． コーパスには，感情タグが付与されており，言葉遣いからの感情推定などのAffective Computingへの応用に用いることができる． コーパスの半自動構築のプロセスは，用例文の自動収集を行い，収集した用例文へのタグ付与，そのあと，人手によるタグ修正・ノイズ文除去作業が行われる． このプロセスにおいて，次の2つの問題がある．第1は，自動収集したコーパスの品質の問題がある．第2に，タグ付与の精度の問題である． 付与したタグが信頼できないものであると，結局，人手での修正作業に時間がかかってしまう． 第1の問題への解決策として，ノイズ文を自動で除去するフィルタリング手法を提案する． また，第2の問題への解決策として，若者言葉のような解析の難しい語が含まれていても感情推定できる手法を提案する． 提案手法の有効性を確認するために，提案したフィルタリングの精度と，提案した感情推定手法の精度評価を行った． 精度評価の結果，従来の辞書のみまたは，機械学習のみの手法と比較し，F1-Scoreの改善がみられ，提案手法の有効性が確認された．

IEEE Transactions on Affective Computing


Reliability Verification and Practical Effectiveness Evaluation of the Nursing Administration Analysis Formulae Based on PSYCHOMS®


Extraction Japanese Slang from Weblog Data Based on Script Type and Stroke Count

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.

若者はWeblogやSNSにおいてよく俗語を用いる．こうした語をどう扱うかが，テキストマイニングなどの分野において，一つの問題となっている．本論文では，若者にとくに用いられる若者言葉と呼ばれる日本語俗語を文字種と画数に着目することにより抽出する手法を提案した．評価実験の結果，文字種を適用した場合に高い抽出精4度が得られることが分かった．


Three Predictions are Better than One: Sentence Multi-Emotion Analysis from Different Perspectives

Emotion prediction has been a core task in affective computing, which aims at finding the thorough human mental states byanalyzing peoples activities. In this paper, we focus on predicting emotions in the public online blogs from different people, byextracting as many reasonable emotions for each blog sentence as possible. Concretely, we consider three different perspectivesfor analyzing the multiple emotions in a sentence: (i) predict sentence emotions by examining the emotion related topics in aglobal sense; (ii) predict the sentence emotions from the context-sensitive word emotions; and (iii) predict sentence emotions byconsidering the emotional significance in the local bag of words. We build different probabilistic models from each perspective, toseparately generate the sentence emotion probabilities. We then integrate these probabilistic models to jointly predict the emotionprobabilities. Because the component models are based on different emotional assumptions with distinct features, the integratedpredictions should predict emotions from more general perspectives and therefore yield better results. In the experiment, weemploy different evaluation criteria to compare the multi-emotion predictions from the single and integrated models. Comparedto the results in the baseline model, our bi-integrated models achieve 8.69% higher Micro F1 and 7.78% higher Macro F1 scores,on average. Moreover, our tri-integrated model acquires 10.00% higher Micro F1 and 9.19% higher Macro F1 scores than thebaseline results, which proves our assumption, and suggests interesting features in the different emotion perspectives. © 2014Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.

感情推定は，アフェクティブコンピューティングにおける核となるタスクの一つである．これは，人間の行動分析による人の精神状態を調べるために必要である．多くのブログ文には，著者の感情が表されているため，我々は，異なる人々のオンラインブログにおける感情推定に着目する．提案手法は，感情に関連するトピックに基づく感情推定と，文脈情報を用いた感情推定，また，局所的なBag of wordsを用いた感情推定を組み合わせたものである．また，我々は，既存の複数の確率的感情推定モデルを結合し，一つの感情に絞り込む方法を提案した．ブログコーパスを対象とした評価実験により，Micro F1スコアとMacro F1スコア双方において，提案手法が従来手法を上回る結果を得ることができた．

IEEJ Transactions on Electrical and Electronic Engineering (TEEE)


Methodology for Developing a Nursing Administration Analysis System

Nursing administration requires a large volume of wide-ranging information, and nurse administrators are limited in their ability to compile and analyze information for nursing administration. The purpose of this study is to create methodology for developing a nursing administration analysis system to aid nurse administrators in performing outcome analysis. In this methodology, information required for nursing administration in the PSYCHOMS? (Psychiatric Outcome Management System, registered trademark) database is analyzed according to the individual needs of nurse administrators. It features a combination of a classification method and an extraction method for obtaining quantitative and qualitative data as information required for nursing administration, and enables nurse administrators to easily obtain analysis results that they directly need. This methodology converts the time required nurse administrators to collect and organize information into time for making considerations in order to devise strategies for improving the quality of nursing care services, and can improve the quality and efficiency of nursing administration. This may lead to an increase of the quality of nursing care services at psychiatric hospitals. This methodology is highly versatile as it can be applied in information management, not only for nursing, but for the entire psychiatric hospital.

看護管理に必要な情報が多岐にわたり膨大となっている．看護管理のための情報の整理や分析は看護管理者の努力だけでは限界があり，看護管理者が行うアウトカム分析を支援することを目的に看護管理分析システムを開発した．看護管理に必要な情報を看護管理者個々のニーズに応じて，PSYCHOMS○R(Psychiatric Outcome Management System, registered trademark)のデータベースに保存されているデータを分析する．その特徴は，分類と抽出の2種類の方法を混合して量的・質的データをアウトプットとして表示することができるため，看護管理に必要な情報を出力できる．看護管理者が直感的に必要と考えたことに対する分析結果を簡便に得ることができる．このシステムを活用することで，看護管理者が情報の収集と整理に要していた時間を，看護サービスの質を改善するための方略を考えるための思考の時間に変換することができるため，看護管理の質と効率性を高めることができる．その結果，精神科看護サービスの質向上につなげることが可能となる．本システムは看護だけにとどまらず，精神科病院全体の情報管理にも応用可能であり，汎用性がある．


Emotion Estimation Method Based on Normalization of Prosodic Features by Using Synthesis Speech

合成音声を用いた特徴量の正規化による感情識別法

本論文では，音声からの感情推定において特徴量を正規化して識別を行う方法を提案する．従来感情識別に用いられている平均パワーやピッチといった韻律的特徴量は，感情による変化だけではなく発話内容そのものによって大きく変化する．そのため，たとえ同じ感情で発話されたとしても発話内容が異なれば異なる韻律となり，感情推定の性能低下を招き得る．そこで本論文では，平静の感情で同じ発話内容を発話した音声からも特徴量を抽出し，それとの差分に注目することで感情による特徴量の変化のみを抽出する方法を提案する．使用している5種類の特徴量(平均パワー，ピッチ等)ごとに減算による正規化，除算による正規化，正規化なし，の三つの方法で正規化し，全ての組み合わせの中で最も性能が向上する組み合わせを探索した．その結果，最適な組み合わせによる識別性能は，正規化を行わない従来法と比較して5.98%向上した．正規化法に関する分析を行ったところ，平均パワーは正規化が必要(演算は減算でも除算でも大きな差はない)であり，一方ピッチとMFCCは正規化なし，残りの二つの特徴量についてはどちらでも性能は大きくは変化しないことがわかった．

The Transactions of the Institute of Electronics, Information and Communication Engineers D

電子情報通信学会論文誌(D)


Pleasant/Unpleasant Filtering for Affective Image Retrieval Based on Cross-Correlation of EEG Features

We propose a sensitivity filtering technique for discriminating preferences (pleasant/unpleasant) for images using a sensitivity image filtering system based on EEG. Using a set of images retrieved by similarity retrieval, we perform the sensitivity-based pleasant/unpleasant classification of images based on the affective features extracted from images with the maximum entropy method (MEM). In the present study, the affective features comprised cross-correlation features obtained from EEGs produced when an individual observed an image. However, it is difficult to measure the EEG when a subject visualizes an unknown image. Thus, we propose a solution where a linear regression method based on canonical correlation is used to estimate the cross-correlation features from image features.

本論文では，脳波特徴を利用した感性的画像検索システムのための，画像の好き嫌い(好悪)を分類する感性的フィルタリング技術の提案を行った．類似画像検索により得られた検索結果画像集合に対し，感性フィルタリングを行う方法として，画像特徴から推定した感性特徴量に基づく，最大エントロピー法(Maximum Entropy Method :MEM)を用いた画像の好悪分類を行う．本論文では，画像を見ている時の脳波から計算して得た相互相関特徴量を感性的な特徴量と考える．しかし，被験者が未知の画像を見るたびに脳波の測定を行わなければならないという問題がある．本論文では，正準相関分析に基づく線形回帰法を用いて画像特徴量から相互相関特徴量を推定するという解決策を提案する．

Applied Computational Intelligence and Soft Computing


Index Compression for Audio Fingerprinting Systems Based on Compressed Suffix Array

In this paper, we propose a method for index compression using a compressed suffix array. Taking advantage of the fact that the repetitive characters occur frequently in higher bits of the sorted audio fingerprint data, the proposed method compresses the index by encoding the 8-bit data sequences by Run Length Encoding. Vertical Coding is also used to compress the array, wherein the positions of the sorted data are stored.

圧縮接尾辞配列というデータ構造を利用し，オーディオ指紋に基づく検索のインデックスを圧縮する手法を提案した．ソートされたオーディオ指紋データが上位バイトは同じ値になりやすいという性質を利用し，同位8ビットに対するランレングス符号により圧縮を行う．また，データ順を示す配列が部分的に単調増加となる性質を利用し，Vertical Codeに基づく配列圧縮を行う．

International Journal of Information and Education Technology (IJIET)


The new growing research eld of Affective Computing (AC) provides a newhorizon for quantitative analysis of human emotional states using IT techniques. In thispaper, a new academic system called nriching Mental Engineering (EME)" is proposedfor the problem of mental health from the view of engineering. EME is being establishedas an academic discipline, by being keenly aware of the poverty of the mind from whichpeople living in modern society suffer. In EME, quantitative measurement of richness ofthe mind is regarded as a central technique. This is measured from the information likesubject physiological data, textual information, behavior, and tone of voice. Meanwhile,EME also systemizes external stimuli by an emotional energy function. The emotionenergy function is proposed to calculate a person's emotional stimuli at a certain pointfrom factors like choice of words, voice, facial expressions, physiological information, andbehavior. Furthermore, an application of EME is illustrated through an analysis of thedepressive tendencies in blogs.

近年の感情コンピューティングにおける分野では，人間の感情状態を，情報処理技術を用いて定量的に分析する手法が提案されている． 本研究では，豊心工学という新しい学問体系を確立し，その技術により，精神の健康の問題を解決することを目的とする．本論文では，豊心工学における分析手法の一例として，ブログにおけるうつ状態を分析するための手法を取り上げる．

International Journal of Innovative Computing, Information and Control


Fast Search Method for Audio Fingerprinting Systems Based on Query Multiplexing

検索質問多重化による高速なオーディオ指紋検索

In music information retrieval, a huge search space has to be explored because a query audio clip can start at any position of any music in the database, and also a query is often corrupted by highly significant noise and distortion. Audio fingerprints have attracted much attention recently for providing compact representation of the perceptually relevant parts of audio signals. In this paper, we propose an extremely fast method of exploring a huge hamming space for audio fingerprinting systems. The effectiveness of our method has been evaluated by experiments using databases of 8,740 real songs and 800 artificially corrupted and 268 real queries.

楽曲検索では，ノイズ等による楽曲の劣化や，検索質問曲の演奏開始位置が不明であることから，一般に膨大な検索空間を探索する必要がある．多くの楽曲検索システムでは，楽曲の音響的・知覚的な特性に基づくオーディオ指紋(audio fingerprint) を特徴量として用いているが，本稿では，オーディオ指紋に適した高速検索手法を提案する．また，8,740 曲の楽曲データベースを用いた評価実験を行い，提案手法の有効性を示した．

IEEJ Transactions on Electronics, Information and Systems

電気学会論文誌C (電子，情報，システム部門誌)


Skelton Pruning Based on the Total Bisector Angle of the End Branches

Skeletonization is an important technique for shape representation, but the skeleton's high sensitivity to boundary noise hampers its application to automatic shape matching. In this paper, we propose a linear-time skeleton pruning algorithm based on the total bisector angle of the end branches. The main idea of the proposed algorithm is to iteratively delete the end branches having the smallest total bisector angle. Since the total bisector angle considers both the local and the global skeleton information, the proposed algorithm removes all redundant branches generated by boundary noise and obtains the ideal skeleton. The experimental results show that the proposed algorithm is highly adaptable, which means that under the same threshold conditions, the pruned skeletons obtained by our proposed algorithm are in accord with human visual perception for most shapes in the MPEG-7 and Tari 56 datasets. Consequently, the pruned skeletons can be applied to automatic shape matching.

画像の骨格化は，画像の形状を表現する上で重要な技術であるが，境界ノイズの影響を受けやすいという欠点があり，形状マッチングをする上で大きな問題点となっている．本論文では，終端枝角の二等分線に基づく，画像骨格線の枝刈りアルゴリズムを提案するとともに，実験においてその有効性を示した．

IEEJ Transactions on Electronics, Information and Systems


Emotional Vector Distance Based Sentiment Analysis of Wakamono Kotoba

In this paper, we propose a method for estimating emotion in Wakamono Kotoba that were not registered in the system, by using Wakamono Kotoba example sentences as features. The proposed method applies Earth Mover's Distance(EMD) to vector of words. As a result ofthe evaluation experiment using 14,440 sentences, higher estimation accuracy is obtained by considering emotional distance between words - an approach that had not been used in the conventional research - than by using only word importance value.

本研究では，日本語俗語(若者言葉)に感情を付与した辞書を自動構築するため，コーパス内において若者言葉と共起する語(主に感情表現)の感情極性ベクトルを用いて，Earth Mover's Distanceによる感情ベクトルを推定する手法を提案する．感情表現への感情極性ベクトルは，感情表現辞書と感情極性対応表を組み合わせることにより付与できる．本手法の評価実験をおこない，単語重要度を用いた従来手法よりも高精度な感情ベクトルが推定できることを明らかにした．


Exploring the Importance of Modification Relation for Emotional Keywords Annotation and Emotion Types Recognition

In this study, we make a scheme to explore the importance of modification relation for the emotional keywords annotation and emotion types recognition. We extract three modification features which are degree words,negative words and conjunctions from the Chinese emotion corpus named Ren-CECps. Beside word and part-of speech,three modification relations are adopted as feature in this study. We have carried out eight experiments with different feature sets for emotional keywords annotation and emotion types recognition in sentence level. Eight basic emotion types have been selected and Conditional Random Fields have been employed as the algorithm. In the part of evaluation, we demonstrate the importance of the modification features and our experiment results show the effectiveness of the modification features for improving the performance of emotional keywords annotation and emotion types recognition.

本研究では感情キーワードの付与と，感情タイプ認識における，修飾関係の重要度を推定する手法を提案した．具体的には，中国語感情コーパスRen-CECpsを用いて，程度を表す語，否定語，接続語の3つの修飾素性を抽出する．実験の結果，文に対する感情キーワードの付与と，感情タイプの認識の精度の改善を達成できた．

International Journal of Intelligent Engineering and Systems


Estimation of word emotions based on part of speech and positional information

Recently, studies on emotion recognition technology have been conducted in the fields of natural language processing, speech signal processing, image data processing, and brain wave analysis, with the goal of letting the computer understand ambiguous information such as emotion or sensibility. This paper statistically studies the features of Japanese and English emotional expressions based on an emotion annotated parallel corpus and proposes a method to estimate emotion of the emotional expressions in the sentence. The proposed method identifies the words or phrases with emotion, which we call emotional expressions, and estimates the emotion category of the emotional expressions by focusing on the three kinds of features: part of speech of emotional expression, position of emotional expression, and part of speech of the previous/next morpheme of the target emotional expression.

近年，自然言語処理，音声信号処理，画像処理，脳波分析などの分野において感情認識技術の研究がおこなわれている．本研究では，日英対訳文パラレルコーパスに感情タグを付与した，日英対訳感情コーパスを対象として，単語への感情付与をおこなうことを目的とする．本手法において，確率的モデルを用いて系列ラべリングをする際に，従来の品詞情報だけでなく，位置情報を用いることで，より高精度な単語感情推定がおこなえることが，評価実験の結果明らかとなった．


Analysis of Wakamono Kotoba Emotion Corpus and Its Application in Emotion Estimation

Recently, there is a lot of research that aims to estimate emotion from text. The meaningsof linguistic expressions used in daily life vary depending on the context in which theyare used. That is to say, the information they contain presents ambiguities. Especiallythe so-called Wakamono Kotoba, Japanese language used by young people containssemantic ambiguities. Such words are usually not included in the existing dictionaries,making the meanings of these words difficult to be recognized. In this research projectwe proposed a method to estimate emotion from sentences that include Wakamono Kotobaby using statistical learning methods such as Na¨ıve Bayes method and Accumulationmethod. The existing research usually focused on learning methods using word or wordN-gram as features. However, such word-based features are insufficient to process WakamonoKotoba because Wakamono Kotoba often cannot be recognized as one semanticword by morphological analysis. In this paper we describe how we constructed the linguisticresource of Wakamono Kotoba emotion corpus to be used for emotion recognitionand introduce the features we obtained from statistical analysis. Our Wakamono Kotobaemotion corpus includes Japanese words used by young people to express emotion. Thesewere mainly gathered from Weblogs that were written by young people from their teensto their twenties.

本研究では，若者言葉感情コーパスを構築し，その分析をおこなう．そして，感情推定への応用について述べる．従来，発話文に感情タグを付与したコーパスは数あれど，文中に含まれる未知語(とくに若者言葉)に着目した研究はほとんどなかった．この理由として，基本的に若者言葉が辞書に載らない語として，軽視されてきたことがある．しかし，Web上のテキストデータが容易に入手できるようになった現在，Web上の話し言葉に近い口語文をリソースとしてテキストマイニングなどの研究をおこなう場面が確実に増加してきている．本研究で提案する若者言葉感情コーパスは，若者言葉を含んだ発話文をブログなどに含まれる発話文から抽出し，手作業により感情タグを付与していくことにより構築する．この作業自体は，人手による判断が重要であり，手作業で行う必要があるため，今後は自動構築手法についても研究していく必要がある．

International Journal of Advanced Intelligence (IJAI)


Retrieval Method of Example Sentence for Intelligent English Composition Support System

Recently the opportunity of reading and writing in English has been greatly increasing. We focused on supporting the Japanese to write English sentence by indicating appropriate example sentences. This paper proposed a method to retrieve such target example sentences by using domain categories and keywords or directly inputting the Japanese sentence.The result of the experiment to evaluate the precision of example sentence retrieval indicated that our technique exceeded the conventional technique by more than 20%.

近年，英語での読み書きの機会が増加している．本研究では例文ベースの英作文支援技術を提案し，システムで用いる例文データベースの効果的な検索手法について，複数の従来手法と比較することにより評価をおこなった．

Information : an International Interdisciplinary Journal


Developing the Method of Server Controlled Outcomes Management and Variance Analysis

This paper is to describe a way to develop the Psychoms process for mental health patient management and variance analysis system has been developed using artificial intelligence. Although it is agreed that there is a need for clinical pathway variance analysis, methods for creating a system are less well defined. The procedure and systematic process described aims to improve patients' quality of life through consistent and timely care. Ultimately, its potential influence is to assist in the improvement of quality health care services. This paper illustrates a method of outcomes management and variance analysis as the prospective development of future research.

本論文では，人工知能を使って開発されたメンタルヘルス患者管理と差異分析システムのためのPSYCHOMSプロセスを開発する方法を記述した．それは，臨床経路差異分析の必要性があることが合意されているが，システムを作成するための方法は，あまりよく定義されていない．このシステムの開発過程において，タイムリーなケアを提供することによって患者の生活の質を改善するための組織的なプロセスと手順が明確にされた．最終的に，その潜在的な影響は，質の高い医療サービスを支援することである．本論文では，今後の研究や前向きの開発などのアウトカム管理と差異分析の方法を示した．

Electronic Notes in Theoretical Computer Science


Evaluation of Emotion Estimation Methods Based on Statistic Features of Emotion Tagged Corpus

Development of information technology has been increasing the chance of interaction between human and computer. Computer used to be a tool and its development had been mainly emphasized on the speed of information processing. However, by understanding not only semantic information but also ambiguous information such as emotion, computer can be a versatile partner for human that can detect emotional state of a person and can response appropriately the emotion. As a primary study of text-based emotion estimation by computer we focused on statistically analyzing the relationship between word emotion and sentence emotion based on the originally created emotion tagged corpus then propose two emotion estimation methods according to the obtained statistic features. The proposed methods are evaluated using a prototype system.

本研究では，発話文にタグ付与をおこなったコーパスを用いた感情推定手法の評価方法を提案する．従来，機械学習を用いた統計的な感情推定手法が提案されてきたが，それらの評価方法については検討されることが少なかった．本研究では，構築した感情タグ付きコーパスを用いて，統計的な特徴を得ることで，2種類の感情推定手法を提案する．プロトタイプを用いた評価実験の結果，提案した手法が有効であることを示した．

International Journal of Innovative Computing, Information and Control


Emotion Estimation Algorithm Based on Interpersonal Emotion Included in Emotional Dialogue Sentences

Emotion recognition aims to make computer understand ambiguous information of human emotion. Recently, research of emotion recognition is actively progressing in various fields such as natural language processing, speech signal processing, image data processing or brain wave analysis. We propose a method to recognize emotion in dialogue text by using originally created Emotion Word Dictionary. The words in the dictionary are weighted according to the occurrence rates in the existing emotion expression dictionary. We also propose a method to judge the object of emotion and emotion expressivity in dialogue sentences. The experiment using 1,190 sentences proved about 80% accuracy.

感情推定は，人間のあいまいな情報である感情をコンピュータに理解させるために必要である．言語・音声・画像などの情報から，人の感情を認識することが，近年のアフェクティブコンピューティングにおける最重要課題である．対話における感情理解のためには，少なくとも1名以上の相手がいる会話において生起する感情に着目する必要がある．本研究では，1190文の発話文を対象として，対人関係に着目し，話者が自分のことをどう呼ぶか，他社のことをどう呼んでいるかという点に基づく感情推定手法を提案する．


Emotion Estimation Algorithm based on Emotion Occurrence Sentence Pattern

感情生起事象文型パターンに基づいた会話文からの感情推定手法

近年の情報処理技術の発達に伴い, 従来の情報処理の分野ではほとんど取り扱われなかった人間の感性をコンピュータで処理しようとする試みが盛んになってきた. 擬人化エージェントや感性ロボットが人のように振舞うためには, 人間が表出する感情を認識し, 自ら感情を表出することが必要である. 我々は, 感性ロボットに応用するための感情認識技術について研究している. 自然言語会話文からの感情推定を行う試みは, 多くの場合, 表面的な感情表現のみに絞つて行われてきた. しかし, 人間の発話時には常に何らかの感情が含まれていると考えられる. そこで, 本稿では, 感情語と感情生起事象文型パターンに基づいた感情推定手法を提案し, 実験システムを構築する. そして, 本手法の有効性を調べるため, シナリオ文を対象にその評価実験を行った.


Emotion Estimation System based on Emotion Occurrence Sentence Pattern

The approach of emotion estimation from the conventional text was for estimating superficial emotion expression mainly. However emotions may be included in humans utterance even if emotion expressions are not in it. In this paper, we proposed an emotion estimation algorithm for conversation sentence. We gave the rules of emotion occurrence to 1616 sentence patterns. In addition, we developed a dictionary which consisted of emotional words and emotional idioms. The proposed method can estimate emotions in a sentence by matching the sentence pattern of emotion occurrence and the rule. Furthermore, we can get two or more emotions included in the sentence by calculating emotion parameter. We constructed the experiment system based on the proposed method for evaluation. We analyzed weblog data including 253 sentences by the system, and conducted the experiment to evaluate emotion estimation accuracy. As a result, we obtained the estimation accuracy of about 60 %.

本研究では，感情が生起する事象について記述した文のパターンと感情パラメータとの対応とを付与したデータベースを構築し，ウェブログデータに対して感情推定をおこなう手法を提案する．本提案手法により，約60%の精度の感情推定精度が得られた．


Analyzing Transformers via Value Matrices

Transactions of the Japanese Society for Artificial Intelligence