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.


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


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.


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.


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


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.

International Journal of Advanced Intelligence (IJAI)


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


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


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．


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．


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)


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．


Analysis of Long-term Market Trend by Text-Mining of News Articles

新聞記事のテキストマイニングによる長期市場動向の分析

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.

Transactions of the Japanese Society for Artificial Intelligence


Dynamic Synonym Candidates Extraction for Searching Documents in a Corpus

コーパス検索支援のための動的同義語候補抽出

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.


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

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

日本データベース学会論文誌


同義語辞書作成支援ツール

同義語辞書作成支援ツール


Integrating Tables on the World Wide Web

Transactions of the Japanese Society for Artificial Intelligence


Analyzing Transformers via Value Matrices