Analysis of Emotional Changes in Twitter Users by Personality Type Before and After the Outbreak of the COVID-19 Pandemic

Proceedings of the 202319th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (FSKD)

Three years have passed since the outbreak of the novel coronavirus pandemic at the end of 2019, and the situation continues to change daily as non-pharmaceutical interventions and associated restrictions are gradually being eased. Even now, drastic changes in aspects of daily living continue to have significant impacts in terms of physical and mental stress on many people. In this study, we analyzed changes in Twitter users comments, thoughts, and actions by conducting sentiment analysis of the content of tweets posted before and after the outbreak of the pandemic. We extracted characteristic expressions and conducted a comparative analysis. In addition, we examined changes in emotions according to users personality types.


Extraction of keywords from disease-fighting blogs by fine-tuning the T5 model

2022 6th International Conference on Natural Language Processing and Information Retrieval

To measure lifestyle-related diseases such as heart disease and diabetes, which are on the increase, information on patients' lifestyles is necessary. With the growth of the Internet culture in recent years, a great deal of valuable information about patients' long fight against the disease, including lifestyle and condition changes, is now being written in the form of blogs and tweets. In this study, keywords related to changes in health status are extracted from "disease-fighting blogs" using Named Entity Recognition system, which is fine-tuned the "T5 Model", one of the latest deep learning and natural language processing models. This method can be applied to a system that informs patients and health care professionals how their lifestylehabits affect their health status.


Emotion and personality estimation of utterances in a dialogue model reflecting personality

Proceedings of 4th International Conference on Technological Competency as Caring in Nursing and Health Sciences 2022

One of the factors that enable humans to interact with AI in a friendlier manner is personality, and since the personality required of AI varies depending on the situation and context, a mechanism is needed that allows AI to switch between different personalities depending on the person with whom he or she is interacting. In this study, we use a Japanese language Transformer Encoder-decoder dialogue model (Japanese-dialog-transformers) and aim to construct a dialogue model that reflects the personality of the target person by fine-tuning the interview dialogue data. By analyzing the output of the proposed dialogue models using the MBTI estimation model and the sentiment analysis model, we evaluate the consistency of the personality and discuss the differences and similarities among the dialogue models.


Affective Analysis and Visualization from Posted Text, Replies, and Images for Analysis of Buzz Factors

Frontiers in Artificial Intelligence and Applications (FAIA), Fuzzy Systems and Data Mining VIII

In this study, we propose a method to visualize the factors that contribute to the buzz phenomenon triggered by Twitter posts. The analysis included tweets, images, and replies. Replies are after-the-fact responses posted in response to a posted tweet and therefore cannot be used to predict buzz phenomena. Therefore, they cannot be used to predict the buzz phenomena. In this study, the tweet body, images, and reply text were feature vectors, and an affective analysis model was constructed. Visualization of the relationship between the sensibility features output from this model and the number of RTs and likes (echo index), which represent the scale of the buzz, will be useful for analyzing the factors behind the popularity. Consequently, the subjective sensibility information with the most likes also tended to have a higher degree of similarity among the sensibility vectors.


A System to Correct Toxic Expression with BERT

Proceedings of the 14th International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management - KEOD

This paper describes a system for converting posts with toxic expression on social media, such as those containing slander and libel, into less-toxic sentences. In recent years, the number of social media users as well as the cases of online flame wars has been increasing. Therefore, to prevent flaming, we first use a prediction model based on Bidirectional Encoder Representations from Transformers (BERT) to determine whether a sentence is likely to be flamed before it is posted. The highest classification accuracy recorded 82% with the Japanese Spoken Language Field Adaptive BERT Model (Japanese Spoken BERT model) as a pre-trained model. Then, for sentences that are judged to be toxic, we propose a system that uses BERTs masked word prediction to convert toxic expressions into safe expressions, thereby converting them into sentences with mitigated aggression. In addition, the BERTScore is used to quantify whether the meaning of the converted sentence has changed in meaning compared to the original sentence and evaluate whether the modified sentence is safe while preserving the meaning of the original sentence.


Music Video Search System Based on Comment Data and Lyrics

Advances in Natural Computation, Fuzzy Systems and Knowledge Discovery(Lecture Notes on Data Engineering and Communications Technologies; LNDECT)

Many people currently use video-sharing services such as YouTube. Keyword search is prevalent in these services. It can be hard to find a video that matches the users' interests using keyword search unless appropriate words are used. In this study, we propose a method for retrieving music videos with similar impressions by analyzing comment data from YouTube viewers and music lyrics. The proposed method converts comments and lyrics into vectors using Word2Vec, and music videos with similar impressions are retrieved using fuzzy c-means clustering According to the mean reciprocal rank (MRR) scores, it was clear that the output of the music videos had the same impression within the top three songs.


Analysis of Lifestyle Habits from Weblogs of Patients with Intractable Diseases Using Deep Learning

Proceedings of International Conference on Electrical, Computer and Energy Technologies (ICECET)

In recent years, patient-centered medicine has become widely accepted. To keep a record of their struggles,patients sometimes publish their diaries (disease blogs) on weblogs to be read by an unspecified number of people. In particular, writing a blog may be helpful in the case of intractable diseases of which there are few cases, and patients often feel isolated because they know few people with the same disease. In this study, we used deep learning technology to analyze the blogs written by patients with intractable diseasesand to determine how they live their daily lives and their feelings. If their lifestyles are understood, the relationship between the physical condition and mental changes of people fighting intractable diseases can be determined, and a system that shows behavioral guidelines for new users fighting similar intractable diseases can be built.


Risk Analysis of Developing Lifestyle-related Diseases based on the Content of Social Networking Service Posts

Innovation in Medicine and Healthcare, Part of the Smart Innovation, Systems and Technologies book series

Japans leading causes of death are cancer, cardiovascular disease, and diabetes, which account for approximately 60% of all deaths. All these diseases are closely related to lifestyle habits such as diet, exercise, and sleep and are called lifestyle-related diseases. Lifestyle-related diseases are characterized by a lack of subjective symptoms, making it difficult to notice the deterioration of the disease. In this study, we analyzed tweets related to diet, exercise, physical condition, and mental state from the contents posted by users on social networking services (SNS) to construct a system that can prevent lifestyle-related diseases more simply. We found that the results of this work can lead to the construction of a system than can detect risk factors leading to the development of lifestyle-related diseases, from the contents of users posts.


Topic Segmentation for Interview Dialogue System

Proceedings of 2021 5th International Conference on Natural Language Processing and Information Retrieval

In this study, topic segmentation was performed by referring to the interview dialogue corpus. Utterance intention tags were added to the existing interview dialogue corpus, and uttered sentences were vectorized using BERT, Sentence BERT, and Distil BERT. In addition, topic classification was performed using the utterance intention tags and the features of the preceding and following uttered sentences. Consequently, the greatest accuracy was achieved when the utterance intention tag was used with DistilBERT


Prediction of Number of Likes and Retweets based on the Features of Tweet Text and Images

The current study aimed to investigate social media trends and propose an analysis method to explore the factors underpinning the buzz phenomenon on Twitter. As it is not always possible to determine the cause of the buzz phenomenon from the text content alone posted on Twitter, we limited the analysis to tweets with attached images and devised an analysis method using both text and images. We investigated whether there is a relationshipbetween the features of both tweet text and its attached images, and how the relationship between these features is related to the number of likes and retweets (RTs) receivedthat is, indicators of popularity. We trained a multi-task neural network that takes the features extracted from the images and text as input, and then outputs the number of likes and RTs before extracting the feature vectors of the same dimension from the two inputs (images and text, respectively) from the middle layer. By calculating the distance between these feature vectors, we analyzed the relationship between the number of likes and RTs. The results revealed that the average vectors of BERT and inceptionresnetv2 served as predictors of the number of likes and RTs. We also found that tweet text with a low number of likes and RTs was short and simple.


Construction of MBTI Personality Estimation Model Considering Emotional Information

Proceedings of the 35th Pacific Asia Conference on Language, Information and Computation (PACLIC35)

The widespread use of social networking services (SNSs) such as Twitter and Facebook in recent years have rendered it easy to interact with various people all over the world.Analysis of the user personality and emotion using features such as the text information and image information on SNSs has been researched.This study analyzes the effectiveness of using emotional information as a feature when building a personality estimation model for Twitter users. The extensively used Myers-Briggs type indicator (MBTI) is used for analysis. A method is proposed for estimating the MBTI personality pattern by extracting emotional information from Twitter and adding it to the characteristics of the distributed expressions of sentences and words.Through evaluation experiments, it is determined whether the emotional information is effective in constructing the MBTI personality estimation model. The obtained results demonstrate that the accuracy is improved when emotional information is included as a feature, compared to the case without emotional information.


Construction of Emotional Similarity Word Embedding Model for Sentiment Analysis

20th Mexican International Conference on Artificial Intelligence (MICAI2021)

We propose a method for constructing a dictionary of emotional expressions, which is an indispensable language resource for sentiment analysis in the Japanese. Furthermore, we propose a method for constructing a language model that reproduces emotional similarity between words, which to date has yet not been considered in conventional dictionaries and language models. In the proposed method, we pre-trained sentiment labels for the distributed representations of words. An intermediate feature vector was obtained from the pre-trained model. By learning an additional semantic label on this feature vector, we can construct an emotional semantic language model that embeds both emotion and semantics. To confirm the effectiveness of the proposed method, we conducted a simple experiment to retrieve similar emotional words using the constructed model. The results of this experiment showed that the proposed method can retrieve similar emotional words with higher accuracy than the conventional word-embedding model.


The Relationship between Personality Patterns and Harmfulness: Analysis and Prediction based on Sentence Embedding

3rd Technological Competency as Caring in the Health Sciences 2021

In this paper, we propose a method for detecting harmful contents in social media. In the Social Networking Service (SNS) the articles and messages posted on these services can be viewed by an unspecified number of unknown people from worldwide. Recently, increasing the number of users who have been forced to close their SNS accounts or have been suffering from mental problems because of blew up SNS triggered by hate speech or bullying remarks in social media. Even if the management of the SNS takes countermeasures, it is difficult to deal with all types of harmful expressions. In this research, we focus on the characteristic that harmful statements need to be judged from the entire sentence, and extract features of harmful expressions using a general-purpose language model.


Lifestyle Analysis via a Corpus of Disease-Fighting Weblogs

Proceedings of 2021 IEEE 2nd International Conference on Pattern Recognition and Machine Learning (PRML 2021)

In recent years, the population of pre-diabetics in Japan has been increasing year by year. Type 2 diabetes is a type of lifestyle-related disease that can be prevented to a certain extent by correcting lifestyle habits. However, by the time we realize that there is a problem with our lifestyle, it may be too late. Therefore, early detection of risk factors for lifestyle-related diseases is important. In this study, we collected the blogs of lifestyle-related disease fighters, set up multiple keyword categories that are considered to be related to risk factors, and constructed a corpus of disease fighting blogs with labels for each category. The results of the evaluation experiments show that our proposed method can be applied to a wide range of topics. As a result of evaluation experiments, our proposed method achieves categorization of keywords and sentences with higher accuracy than the simple method.


Visualization of the Artist Relations Using Twitter User Profiles

Frontiers in Artificial Intelligence and Applications (FAIA), Fuzzy Systems and Data Mining VI,


ASCII Art Classification Model by Transfer Learning and Data Augmentation

Frontiers in Artificial Intelligence and Applications (FAIA), Fuzzy Systems and Data Mining VI

In this study, we propose an ASCII art category classification method based on transfer learning and data augmentation. ASCII art is a form of nonverbal expression that visually expresses emotions and intentions. While there are similar expressions such as emoticons and pictograms, most are either represented by a single character or are embedded in the statement as an inline expression. ASCII art is expressed in various styles, including dot art illustration and line art illustration. Basically, ASCII art can represent almost any object, and therefore the category of ASCII art is very diverse. Many existing image classification algorithms use color information; however, since most ASCII art is written in character sets, there is no color information available for categorization. We created an ASCII art category classifier using the grayscale edge image and the ASCII art image transformed from the image as a training image set. We also usedVGG16, ResNet-50, Inception v3, and Xceptions pre-trained networks to fine tune our categorization. As a result of the experiment of fine tuning by VGG16and data augmentation, an accuracy rate of 80% or more was obtained in the human category.


Keyword Extraction from TV Program Viewers Tweet Based on Neural Embedding Model

In recent years, young people have not been watching television (TV) as much as they used to. This is mainly because a number of TV programs are very long and/or have limited viewing times. Recently, individuals have been actively posting live-action tweets on Twitter to comment on TV content while watching programs in real time. In this study, we propose a method for extracting key phrases related to the event scenes of TV programs using live tweets, and we propose a scene search system that aims at efficient TV program viewing. The experimental results indicated that the program contents were estimated with an error of approximately 5% to 10% with respect to the program time. In addition, the extracted key phrases were visualized for each event scene category using the t-SNE algorithm.


Towards Automatic Dental Diagnosis System Based on Deep Learning

Proc. IEEE International Conference on Natural Language Processing and Knowledge Engineering


Construction of Annotated TOBYO Blog Corpus for Lifestyle Disease Analysis of Diabetic Patient

Proceedings of The 15th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE20)

In this study, we target to analyze weblogs authored by diabetes patients describing their struggle against their disease. In order to build a corpus for analysis, we annotate tags to the important keywords/key phrases such as blood glucose level, diet and emotions in collected weblog data. We aim at constructing a labeled lifestyle disease weblog corpus (called as TOBYO weblog corpus) to extract useful information from patients' weblogs of against illness. For example, to improve our lifestyle, it would be very useful to know how we should live every day, what kind of diet we should eat, how much exercise is appropriate, and so on. In our study, we organize the tagged data and take statistics on part-of-speech combinations. We set the top N thresholds in the rank of the frequency of the part-of-speech and create a system to exclusively pre-process the key phrases that are ranked as more frequent part of speech combinations. The extracted key phrase information is vectorized by a model of BERT (bidirectional encoder representation from a transformer), then the key phrases are sorted out by making a model for category prediction that uses two inputs (BERT vector and character frequency vector). To evaluate our proposed method, we conduct cross validation test and open test by using several machine learning algorithms. In comparison with the baseline classification method, which is a simple and ordinal neural network method, our method can achieve higher performance.


Construction of lifestyle disease weblog corpus and evaluation of keyword classification model

International Forum on Advanced Technologies 2020 (IFAT2020)


Emotional State Estimation by Dialogue History and Sentence Distributed Representation

Proceedings of 2019 6th IEEE International Conference on Cloud Computing and Intelligence Systems(CCIS2019)

The paper presents a novel approach to predict humans emotion in the dialogue. We target the scenario dialogue corpora. The corpora are annotated by some subjects with emotion tag. We use sentence embedding by Bidirectional Encoder Representations from Transformers as versatile and rich feature. By acquiring multi-context information from dialogue text, the accuracy of emotion prediction can be improved. As an experiment by machine learning algorithm such asdeep neural networks for the proposed method, we achieved higher accuracy than the method using embedding feature from utterance itself.


Kansei Mapping of Color Images Using Earth Mover's Distance

International Conference on Software Technology and Engineering (ICSTE2019)


An Approach for Conversion of Japanese Emoticons into Emoji Based on Character-Level Neural Autoencoder

Frontiers in Artificial Intelligence and Applications

In this paper, we propose a method for converting Japanese emoticons into emoji. The method creates a model that translates text into emoji using training neural networks with a character-based feature; the model determines the positive/negative/neutral polarity of the emoji from the text. By extracting a feature vector from the hidden layer of the model, we calculate the similarity between the input sentence and the text annotated with emoji in the database; the conversion candidates come from the emoticon in the input text. In comparison tests of the proposed method and a word-level feature method that uses a word distributed representation vector, the emoji emotion polarity-based model achieved a maximum accuracy rate of 90.0%, representing an 8% improvement over the word-level feature method.


Emotion Analysis on Weblog of Lifestyle Diseases

Proceedings of the 2019 International Symposium on Signal Processing Systems (SSPS2019)

In this paper, we investigate the relation between patient lifestyle and emotional state transition by analyzing weblog articles written by patients with diabetes. To conduct our analysis, we use blood sugar level and body weight to indicate the disease condition, together with meal keywords and sentences that express feelings in the selected weblogs. A corpus annotated with a tag to word units in the target articles is constructed and a method of keyword classification based on the constructed corpus is proposed and evaluated. Finally, we analyze whether the indicated emotion is positive or negative in sentences expressing emotion in the corpus and investigate the relation between the emotion and a change in blood sugar level.


Analysis of Reply-Tweets for Buzz Tweet Detection

Proceedings of 33rd Pacific Asia Conference on Language, Information and Computation (PACLIC33)

In this study, we propose a method for predicting whether a tweet will create a buzz on the Internet by examining tweeted replies posted by others. We also investigate the distinguishing characteristics of replies to buzz tweets by analyzing feature amounts. Our proposed method first converts each reply tweet into a vector expression using a word distributed representation or some other vectorization method. We then apply a machine learning method for binary classification todetermine whether the reply is to a buzz tweet or a non-buzz tweet. We classify the target tweet into buzz or non-buzz categories by comparing the total buzz and non-buzz scores produced by the classifier. The proposed method using StarSpace achieved 93.1% F1-score, while an approach that used number of retweets and number of favors (likes) achieved 77.8% F1-score. Wealso found that there are a number of words that are characteristic of buzz tweet replies anda number of words that are characteristic of non-buzz tweet replies.


Personality Analysis on Dialogue System for Emotional Interaction Robot

Conference Proceedings of Asia Pacific Society for Computing and Information Technology 2019 Annual Meeting (APSCIT 2019 Annual Meeting)

In this study, we proposed the personality analysis method for dialogue system towards realization of emotional interaction robot. To achieve smooth communication between human and robot in dialogue, it is necessary that overcome feeling of strangeness by generating dialogue utterance based on consistent personality. Firstly, we extract the wording feature from utterance texts on social media. To obtain relation between the wording feature and personality ego-state level, we use sentence level embedding feature and create state level estimator for each ego-state. Next, we evaluate the ego-state level estimator with an evaluation test set. Finally, we use this estimator for analysis of chat-bot dialogue. By comparing the existing chat-bot dialogue and user utterance, we consider about non-reality of current chat-bot dialogue generation.


Research on statements in tweets and sleep time of classification

the 3rd International Conference on Natural Language Processing and Information Retrieval


Extended Dataset for Food Image Classification Based on Twitter

Hong Kong International Conference on Engineering and Applied Science (HKICEAS 2018)


Modeling Relations Between Profiles and Texts

Proceedings of the 14th the Asia Information Retrieval Societies Conference (AIRS 2018), LNCS 11292

We propose a method to model Twitter texts and user profiles simultaneously by considering the relations between the texts and profiles to obtain the distributed representations of the words in both.


ASCII Art Category Classification based on Deep Convolutional Neural Networks

Proceedings the 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

In text-based communications on social media, emoticons, emojis, and stamps provide important ways to convey emotion and facilitate smoother communication in a short text. As non-verbal expressions, the characters in ASCII Art have no inherent meaning. However, by using a characters position or shape, emotion or intention can be expressed visually. Until now, it has been difficult to classify ASCII Art using the ASCII Art characters. In this paper, we propose a method for classifying ASCII Art by converting ASCII Art into images and using deep learning. In an evaluation experiment, the proposed method was able to achieve a classification success rate as high as 97%.


Transfer Learning Based on Utterance Emotion Corpus for Lyric Emotion Estimation

Proceedings of 2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)

In prior research, there are various emotion estimation approaches that are corpus-based, dictionary-based, or rule-based. However, it is necessary to prepare language resources suitable to each domain, which will incur substantial cost. In this paper, we propose an approach to estimate emotion of lyric phrase by using transfer learning. We applied the existing utterance emotion corpus to the lyric emotion estimation task. As the result of an evaluation experiment, we obtained higher accuracy than the baseline method using a simple bag of words feature tf-idf weighted vector and logistic regression model.


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

2018 10th International Conference on Software Technology and Engineering


Emoticon-based emotion analysis for Weibo articles in sentence level


Classification of Emoji Categories from Tweet Based on Deep Neural Networks

Proceedings of the 2nd International Conference on Natural Language Processing and Information Retrieval (NLPIR2018)

In this paper, we describe the sentiment analysis method from tweets based on emojis category. Many of existing study about sentiment analysis focused on the emotional expressions included in sentence. However, because there are various kinds of emotional expressions, such as Internet slang, it cannot be constructed that the fixed emotional expression dictionary. The most of the methods based on corpus and machine learning, its performance is quite depended on the quality of annotation. Therefore, we attempt to use categories which are expressed by emoji as sentiment label instead of manually annotated labels. Our proposed method uses automatically annotated category label by emoji which is annotated to sentence, and train word embedding feature by deep neural networks. As the result of the experiment, our proposed method overcome the simple word feature based method.


Emotion Recognition from Emoticons using Convolutional Neural Networks

Proceedings of 2nd Technological Competency as Caring in the Health Sciences 2018

In recent years, text communication has been grown steadily so that all kinds of daily contacts could be conducted now through Internet based text messaging services. As an advantage of text-based communication via the Internet, we can quickly interact with anyone wherever we are. At the same time, since it is difficult to express emotions with each other only with text, the chance of adding non-verbal information such as photos to text is increasing. Among the non-verbal information, there are emoticons in expressions that have been developed by Internet bulletin boards, chat, e-mail, etc. In particular, the types of emoticons used in Japan are diverse, even now; various types of emoticons continue to increase. There have been studies on these emoticons until now, which have become increasingly important in recent years. In this paper, we propose a method to classify facial expressions expressed from emoticons by letting deep convolutional neural networks. When recognizing emotions from emoticons, it is not known that seeing emoticons in character units is very effective. Information such as role positioning and coexistence roles is necessary. Characters in emoticons differ depending on font it also gives different impressions depending on context. In order to realize semantic analysis / emotion analysis considering emoticons, it is necessary to provide a corpus that records various kinds of emoticons and a database that gives information such as meanings and emotions expressed by emoticons. However, including all the emoticons is difficult, and since the emoticons are more vague than ordinary language expressions, that is why the construction of the dictionary is delayed. In this paper, we propose a method to classify the emotions of the emoticons by letting the deep convolutional neural network learn the features of the emoticons. By treating the emoticon as an image, the visual features can be captured and it is considered that natural recognition can be performed instead of processing like a character string. In addition, since the emoticon characterizes the facial expression with a character string, there is a problem that the impression is different only when the used fonts are different from each other, and therefore, when different fonts are used, how the classification accuracy is changed, we consider whether to do so.


Sentence Emotion Classification for Intelligent Robotics Based on Word Lexicon and Emoticon Emotions

Proceedings of the International Conference of Intelligent Robotic and Control Engineering


Visualization of the occurrence trend of infectious diseases using Twitter

Proceedings of the 11th edition of the Language Resources and Evaluation Conference (LREC 2018), (poster)

We propose a system for visualizing the epidemics of infectious diseases. We apply factuality analysis both to disease detection and location estimation for accurate visualization. We tested our methods for several infectious diseases, and show that our method performs well on various diseases.


Predicting facial expression from emoticon based on convolutional neural networks

4th International Forum on Advanced Technologies (IFAT 2018)

Text communication has been developed in recent years, all kinds of daily contacts are through the SNS regardless of age, gender and active. As an advantage of text-based communication via the Internet. There is a point that you can quickly respond to anyone wherever you are. In this paper, by letting the deep convolution neural network learn the image features of emoticons. We propose a method to classify by emotion expressed by emoticons. By treating emoticons as images, it is possible to capture visual features and think that natural identification can be made rather than handling as character strings.


The Effectiveness of Image Data Augmentation for Deep Learning

Conference Abstract of the 3rd International Conference on Multimedia and Image Processing


Visual Similarity for ASCII Arts Using Image Features

Proceedings of the 2018 2nd International Conference on Machine Vision and Information Technology (CMVIT 2018)

Communication with various types of non-verbal expressions has been increasing on Social Networking Services. Among them, ASCII art is a character-based design technique to express human emotions. In many cases, ASCII arts are generated from original images by mapping some region of pixels to a certain character. In the field of natural language processing, ASCII arts are treated as sequences of characters. In addition, there have been few studies that aim to analyze or classify ASCII arts. In this paper, we extract characteristics of ASCII arts using image shape features. Our image shape-based features provide visual similarity between ASCII arts that are invariant across image scale and characters used in ASCII arts. Moreover, the proposed method enable us to compare visual similarity between ASCII arts and original images. To extract shape features from ASCII arts, we use image features of original images by converting ASCII arts to images. In the experiment, we compared visual similarity between ASCII arts and original images by using Histograms of Oriented Gradients(HOG). Our experimental result shows that the proposed method is effective to compare visual similarity between ASCII arts and original images.


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

Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering(NLP-KE'17)

In this paper, we proposed a model to classify reviews based on review data of different media. Recently, researches have been actively made on transfer learning between different domains with various kinds of big data as a target. Evaluation expressions are usually different in different domains, and this becomes a barrier for reputation analysis. Users usually use different linguistic expressions to refer to the creative works of each media. For example, media such as "anime", "comics", "games" and "movies" have different terms or expressions to be described their creative works. These differences can be considered showing features of each medium and such differences should be found in other expressions as well as in evaluation expressions. We analyzed what effect such differences would cause to classication accuracy by conducting transfer learning between review data ofdifferent media. In this paper, we proved compatibility between original (pre-transferring) and target (post-transferring) media for each medium of the creative works by constructing a review classication model. As a result of evaluation experiments, we could more accurately estimate review score without using SO-Score for training of review fragment based on Long Short Term Memory (LSTM) than the SO-Score based method.


Index Generation of BGM Video Based on Distinctive Comments

The purpose of this study is to generate indexes to the BGMs based on the distinctive comments annotated to the BGM videos posted on Nico Nico Videos. Our proposed method detects the end/start positions of the BGMs considering the increase/decrease of the number of the comment data, the comments distinctive to the songs and the exclamatory expressions annotated to the videos. The evaluation result showed that the proposed method could generate correct indexes within 10 seconds error for the BGM videos with length of less than 40 minutes and with annotation of over 30,000 comments.


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

In this study, we focus on a representative SNS, Twitter and target emotion estimation from tweets posted on Twitter. we construct emotion estimation models applicable to each gender. It is considered that there would be different word usage tendencies depending on gender. Therefore, we analyze what difference exists in usage tendency of slangs or emoji, which are thought to be related to emotion, depending on gender. Then, we propose a method to improve emotion estimation based on neural networks by usingdistributed representation models for each gender. As the result of the evaluation experiments, the higher estimation accuracy was realized by training with Deep Convolutional Neural Networks using word's distributed representation as feature than by training with Feed Forward Neural Networks.


Facial Expression Classification Based on Shape Feature of Emoticons

Proceedings of 1st International Conference on Machine Learning and Data Engineering (iCMLDE2017)

Emoticons are used in the situation of textual communication such as web mails and internet forums. Many of the existing studies dealing with classification or extraction of emoticons regard emoticons as a kind of character string and focus on what characters constitute the emoticons or how they are lined up. However, emoticons are used to express human facial expressions, and characters constituting them represent various facial parts such as eyes, nose, mouth, etc. Such characters can be identified as different facial parts depending on their positions, and facial expressions are thought to be represented by the combinations of their shape features. In this study, we classified the facial expressions of emoticons by focusing on the shape features of those emoticons. To deal with shape features of emoticons, we converted emoticons, which are text data, to image data. Emoticons are mainly formed by line segments of characters, and use only black and white colors. Therefore, other factors such as colors and shades were not considered as the feature to classify the facial expressions. In the experiments, we used image features that did not require color information. As the result of comparative experiment with the 1-nearest neighbor method using character features, the facial expression recognition rate is 52% when using the Histograms of Oriented Gradients(HOG) was used as image feature. By this result, proposed method improved recognition rate by 2 % than using baseline.


Gender Estimation from Facial Images by Deep Neural Network

Proceedings of Annual Conference on Engineering and Applied Science

In this paper, we collect new learning data and learned this by the existing neural network in order to exceed the accuracy of the model of the existing research. As a result, we succeeded in improving the accuracy by increasing the learning data. However, we discovered that performance declined due to biased learning data. The future task is to further improve accuracy and Research the relationship between bias and accuracy of learning data.


Emotion Recognition of Emoticon Based on Character Embedding

9th International Conference on Software Technology and Engineering (ICSTE2017)

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.


Face Identification Based on Deep Convolutional Neural Networks

9th International Conference on Graphic and Image Processing


Flame Prediction Based on Harmful Expression Judgement Using Distributed Representation

Book of Abstracts Proceeding of Engineering & Technology, Computer, Basic and Applied Sciences (ECBA-2017)

In recent years, flaming on social media has been a problem. To avoid flaming, it is useful for the system to automatically check the sentences whether they include the expressions that are likely to trigger flaming or not before posting messages. In this research, we target two harmful expressions. There are insulting expressions and the expressions that are likely to cause a quarrel. Firstly, we constructed an original harmful expression dictionary. Because a large cost requires to collect the expressions manually, we constructed the dictionary semi-automatically by using word distributed representations. The proposed method used distributed representations of the harmful expressions and general expressions as features, and constructed a classifier of harmful/general based on those features. An evaluation experiment found that the proposed method could extract harmful expressions with accuracy of approx. 70%.On the other hand, it was found that the proposed method could also extract unknown expressions, however, it wrongly extracted non-harmful.


Distributed Representations for Words on Tables

Proceedings of the 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017), Part-I (LNAI 10234)

We consider a problem of word embedding for tables, and we obtain distributed representations for words found in tables. We propose a table word-embedding method, which considers both horizontal and vertical relations between cells to estimate appropriate word embedding for words in tables. We propose objective functions that make use of horizontal and vertical relations, both individually and jointly.


Acceleration of Similar Word Search in Distributed Representation

Proceedings of the 18th International Conference on Computational Linguistics and Intelligent Text Processing (CICLING 2017) (poster)

We report our study on accelerating search for similar words in distributed representation using cosine similarity measure.Our main idea is to convert similarity value to Euclidean distance, and apply the branch-and-bound approach to distance calculation itself, which means to terminate distance calculation when the current value exceeds the current N-th best similarity value when N is the size of final list of words similar to the query.We also apply other techniques for speeding up such as converting vector representation using PCA and reduction of adding and multiplying operations in distance calculation.


A Comparison of Face Recognition Features Derived from Convolutional Neural Networks

Conference Abstracts of the 2017 International Conference on Frontiers of Image Processing


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

The 11th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE'16)


Improvement of the Histogram for Infrequent Color-Based Illustration Image Classification

Fuzzy Systems and Data Mining II, Frontiers in Artificial Intelligence and Applications


Table Topic Models for Hidden Unit Estimation

Proceedings of the 12th Asia Information Retrieval Societies Conference (AIRS2016), LNCS 9994

We propose a method to estimate hidden units of numbers written in tables. We focus on Wikipedia tables and propose an algorithm to estimate which units are appropriate for a given cell that has a number but no unit words. We try to estimate such hidden units using surrounding contexts such as a cell in the first row. To improve the performance, we propose the table topic model that can model tables and surrounding sentences simultaneously.


Effect of Users Attribute on Emotion Estimation from Twitter

Proceedings of the 2nd IEEE International Conference on Computer and Communications

This study focused on emotion estimation fromutterances on Twitter and analyzed the differences caused forestimation according to the attributes of users. SNS userspossess various user attributes. If we could clarify the tendencyfor each attribute to involve emotion estimation, we would beable to create an emotion estimation model suitable for eachattribute. This study considered two attributes, sex and job, andinvestigated the tendency of emotion estimation based on anemotion estimation model constructed by machine learning. Inthis paper, as a feature for machine learning, we used asentence vector that was obtained by the summation of theword distributed expressions. As a result of the evaluationexperiment using a method based on the nearest neighbormethod, we obtained 0.4 higher estimation accuracies thanusing a baseline method based on a simple Bag of Words model.


Judgment of Slang Based on Character Feature and Feature Expression Based on Slangs Context Feature

Communications in Computer and Information Science

This paper proposes a method to distinguish standardwords from slang words using information from the characters comprising thecharacter string. An experiment testing the effectiveness of our method showeda 30 % or more improvement in classification accuracy compared to the baselinemethod. We also use a contextual feature related to emotion to expand theunregistered slang word in the training data into other expressions and proposean emotion estimation method based on the expanded expressions. In our experiment,successful emotion estimation was obtained in nearly 54 % of the cases, anotably higher rate than with the baseline method. Our proposed method wasshown to have validity.


Face Identification based on Deep Learning Using Low Pixel Images

International Conference on Engineering & Technology, Computer, Basic & Applied Sciences


Lyric Retrieval System Based on Word's Intimacy Value


An Approach to Refine Translation Candidates for Emotion Estimation in Japanese-English Language

Proceedings of the 7th International Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management

Researches on emotion estimation from text mostly use machine learning method. Because machine learning requires a large amount of example corpora, how to acquire high quality training data has been discussed as one of its major problems. The existing language resources include emotion corpora; however, they are not available if the language is different. Constructing bilingual corpus manually is also financially difficult. Wepropose a method to convert a training data into different language using an existing Japanese-English parallel emotion corpus. With a bilingual dictionary, the translation candidates are extracted against every word of each sentence included in the corpus. Then the extracted translation candidates are narrowed down into a set of words that highly contribute to emotion estimation and we used the set of words as training data. As theresult of the evaluation experiment using the training data created by our proposed method, the accuracy of emotion estimation increased up to 66.7% in Naive Bayes.


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

the 10th International Conference on Natural Language Processing and Knowledge Engineering


Sensibility Estimation Method for Youth Slang by Using Sensibility Co-occurrence Feature Vector Obtained from Microblog

Proceedings of the 2015 IEEE International Conference on Computer and Communications (ICCC2015)

Social networking sites such as Twitter provide more opportunities to express what people think or intend in short text. In short text, abbreviations such as ASAP or joinus and emoticons are often used. Because these expressions are not registered into the existing dictionaries, these are analyzed as unknown expressions. That can be a bottleneck for improving accuracy of reputation analysis in text mining. To use context for unknown word clustering is a major method, however, it usually requires word segmentation process and it has weakness for split errors of unknown expressions such as youth slang. In this paper, we proposed a method to obtain the appropriate context even though unknown expressions cause split errors and estimate sensibility expressed in the text. Because the dimensions of the obtained context vector were enormous, we also proposed a method to create a feature vector based on the co-occurrence of the sensibility words as simple expression with low dimension. As an evaluation experiment, the proposed method showed certain accuracy even with the small training data.


Estimate the Intimacy of the Characters Based on Their Emotional States for Application to Non-Task Dialogue

Proceedings of the 6th International Conference on Affective Computing and Intelligent Interaction (ACII2015)

Non-task-oriented dialogue requires someschemes that enable smooth and flexible conversations with auser. For example, it would be possible to go beyond the closedrelationship between the system and the user by consideringthe users relationship with others in real life. In this paper,we focused on the dialogue made by the two characters in adrama scenario, and tried to express their relationship with ascale of intimacy degree. There will be such various elementsrelated to the intimacy degree as the frequency of response to theutterance and the attitude of a speaker during the dialogue. Wefocused on the emotional state of the speaker during the utteranceand tried to realize intimacy estimation with higher accuracy. Asthe evaluation result, we achieved higher accuracy in intimacyestimation than the existing method based on speech role.


Emotion Recognition for Sentences with Unknown Expressions based on Semantic Similarity by Using Bag of Concepts

Proceedings of the 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD'15)

In studies of emotion estimation from text, varietiesof methods have been attempted such as emotional expressiondictionary or sentence structure dictionary and emotion corpus.However, most of these methods targeted the expressions includedin the existing morphological analysis dictionaries, as a result,they did not pay enough attention to unknown words, especiallynewly coined words. In Japan, the growth of Internet communicationsites such as weblogs and social networking sites broughtyounger people especially in teens and in their 20s to create newwords and use them very often. We prepared an emotion corpusby collecting weblog article texts including new words, analyzedthe corpus statistically, and proposed a method to estimateemotions of the texts. Most slang such as Youth Slang is tooambiguous in sense classification to be registered into the existingdictionaries such as thesaurus. To cope with these words, wecreated a large scale of Twitter corpus to calculate sense similaritybetween words. We proposed to convert unknown to sense classid to process the words that were not included in learning data.We defined this as a method using Bag of Concepts as feature. Asa result of the evaluation experiment using several classifies, theproposed method was proved robustness for unknown expression.


Extracting Corpus-Specific Strings by Using Suffix Arrays Enhanced with Longest Common Prefix

Proceedings of the 10th Asia Information Retrieval Society Conference (AIRS 2014), LNCS 8870

We propose a new term extraction algorithm that considers all of the substrings as term candidates.Our algorithm uses a suffix array as the data structure that emulates the suffix tree of the corpus.We use two scoring functions, one of which is used to detect good substring boundaries as linguistic chunksand the other is to find domain-specific phrases and combine them with a re-ranking approach.Experiments show that the proposed all-substring term extraction algorithm shows good performancefor highly-frequent terms compared with the baseline algorithm that uses a morphological analyzer in the preprocessing step.


Reranking the Search Results for Lyric Retrieval Based on the Songwriters' Specific Usage of Words

Electronics, Communications and Networks IV Proceedings of the 4TH Internationa Conference on Electronics, Communications and Networks, 12 15 December 2014, Beijing, China

In this paper, we proposed a method to search lyrics by considering tendency of the words used by the songwriters, and aimed to improve the search accuracy. The existing methods of query expansion for document retrieval tended to focus on the words co-occurrence relation obtained from large amount of text data or to focus on synonymy relation of words to expand query. However, lyrics have different characteris-tics from business documents or weblog articles. For example, lyrics contain a lot of expressions made with different combinations of words compared to other text styles. Therefore, it is not always effective to focus on co-occurrence relation or semantic relation on thesaurus. In this paper, we were on the assumption that the tendency of the words used in the lyrics are different among songwriters. And, we constructed a data of the important words for each songwriter. We used this important word list made for each songwriter to add weight on the search results by n-gram. According to the weights, we rearranged the rank order of the search result aiming to improve the accuracy. We conducted an evaluation experiment using the artificially-created fragment of lyrics including errors as search query, and found that the proposed method improved MRR compared to the baseline method.


Emotion Predicting Method Based on Emotion State Change of Personae according to the Other's Utterance

Proceedings of the 3rd IEEE International Conference on Cloud Computing and Intelligence Systems(CCIS2014)

It is a very difficult task to predict others emotion from dialogue flow for dialogue system or robot, however, that is a natural task for human.As the reason, because of the emotion of human changes at all times when he / she have a conversation with other people, these changes would be considered that oc-curred by responding internal emotional state to stimulus from external world .In the researches about dialogue system, most of them are superficial feature of utterance for question-answering system, thus, internal emotional state and stimulus from external world.In this paper, we focused on the scenario dialogue sentences. We annotated the various information to the corpora for helping analyze dialogue flow or human relationships and constructed the scenario emotion corpus. We discussed that the others utterances or emotions how effect to emotion changes by analyzing this cor-pora.As the method to predict the others emotion changes from self-emotion, we proposed the predicting method based on the emotion predicting weight. As the result of evaluation experiment, the effectiveness of the proposed method could be confirmed.


Unsupervised Analysis of Web Page Semantic Structures by Hierarchical Bayesian Modeling

Proceedings of the Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD) 2014, Part II, LNAI 8444

We propose a Bayesian probabilistic modeling of the semantic structures of HTML documents.We assume that HTML documents has logically hierarchical structures and model them as links between blocks.These links or dependency structures are estimated via sampling methods. We use hierarchical Bayesian modeling where each block is given labels like ``heading''or ``contents'', and words and layout features (i.e., symbols and HTML tags) are generated simultaneouslybased on these labels.


Identifying who drew the illustration focusing on the eyes of the characters

Proceedings of 20th Korea-Japan Joint Workshop on Frontiers of Computer Vision

In this paper, we aimed to automatically identify who draw the of illustration image. Designs of illustration image differ depending on the illustrators. Also, it is difficult to find out common characteristics from the several illustrations drawn by illustrator. We tried to identify who drew the illustration by detecting distinctive habits of the illustrator from his/her works and by using them as the feature. To obtain the feature, we focused on the area of eyes of the illustrated characters. We obtained the edge information from there and calculated percentage of the edges. We call this percentage a "Line Percent" and considered it as the unique feature quantity of the illustrator. From an experiment, the average recall was 35 % and some of the recalls were over 90%. This result suggested that we could identify who draw the illustration images to some extent by using Line Percent as the feature.


Improvement of Indexing Methods for Audio Fingerprinting Systems

International Conference Data Mining, Civil and Mechanical Engineering (ICDMCME'2014)

In music retrieval methods based on audio fingerprints, a large database is required in order to compare the fingerprints extracted from the query. It is necessary to develop an efficient search method. In this paper, we propose a method for index compression using a compressed suffix array. The proposed method compresses the index by encoding the 8-bit data sequences by Run Length Encoding. We also use Fibonacci Hashing function which can adjust the size of hash table according to the capacity of memory, provide a good distribution of hash value, and save memory.


A Fast Retrieval Algorithm Based on Fibonacci Hashing for Audio Fingerprinting Systems

Proceedings of the 2013 International Conference on Advanced Information Engineering and Education Science

In audio fingerprinting system, the database consists of hundreds of millions of sub-fingerprints. How to find out the most similar audio in the shortest time and use less memory in huge repository of sub-fingerprints is a hot topic of research. Recently, Philips introduced an effective search method based on hash table. In this paper, a fast retrieval algorithm based on the Fibonacci Hashing is proposed as an extension of Philipss method. The algorithm uses Fibonacci Hashing function which can adjust the size of hash table according to capacity of memory, provide a good distribution of hash value, and save memory. The performance of presented algorithm has been evaluated by experiments.


Mining Numbers in Text Using Suffix Arrays and Clustering Based on Dirichlet Process Mixture Models

Proceedings of PAKDD-2010 (The 14th Pacific-Asia Conference on Knowledge Discovery and Data Mining), Springer (LNAI 6119)


Person Name Disambiguation by Bootstrapping

Proceedings of SIGIR-2010 (the 33rd Annual ACM SIGIR Conference)


Gram-Free Synonym Extraction via Suffix Arrays

Proceedings of AIRS 2008 (the Fourth Asia Information Retrieval Symposium), Springer (LNCS 4993)


Person Name Disambiguation in Web Pages using Social Network, Compound Words and Latent Topics

Proceedings of PAKDD2008 (The 12th Pacific-Asia Conference on Knowledge Discovery and Data Mining), Springer (LNAI 5012)


Knowledge Discovery of Semantic Relationships between Words Using Nonparametric Bayesian Graph Model

Proceedings of KDD-2008 (Fourteenth International Conference on Knowledge Discovery and Data Mining), ACM


Web Document Parsing: A New Approach to Modeling Layout-Language Relations

Proceedings of the 9th International Conference on Document Analysis and Recognition (ICDAR2007), IEEE Computer Society


NAYOSE: A System for Reference Disambiguation of Proper Nouns Appearing on Web Pages

Proceedings of AIRS2006 (Asia Information Retrieval Symposium), Springer (LNCS 4182)


Reformatting Web Documents via Header Trees

Proceedings of the 43rd ACL2005 Poster/Demo Session


Automatic Term Extraction based on Perplexity of Compound Words

Proceedings of IJCNLP 2005, Springer (LNAI 3651)


Specification Retrieval -- How to Find Attribute-Value Information on the Web

Proceedings of ijcNLP (International Joint Conference of Natural Language Processing),(Springer LNAI 3248)


Extracting Attributes and Their Values from Web Pages

Proceedings of the ACL-02 Student Research Workshop


Extracting ontologies from World Wide Web via HTML tables

Proceedings of the Pacific Association for Computational Linguistics (PACLING 2001)


A method to integrate tables of the World Wide Web

Proceedings of the International Workshop on Web Document Analysis (WDA 2001)


Efficient FB-LTAG Parser and its Parallelization


LiLFeS -- Towards Practical HPSG Parsers

Proceedings of the 17th International Conference on Conmupational Linguistics and the 36th Annua Meeting of the Association for Computational Linguistics, (COLING-ACL 98)


Hybrid Recommender System for Anime with Integrated Emotion Analysis from User Reviews --- Improving Recommendation Precision and Catering to Diverse Tastes by Extracting Emotional Insights

レビューコメントに基づく感情情報を統合したハイブリッドアニメ推薦システム --- 感情抽出による推薦精度向上および多様な嗜好への対応

第26回日本感性工学会講演論文集

第26回日本感性工学会講演論文集

This study aims to develop a recommendation system for the anime market to help users navigate the overwhelming number of titles and find ones that match their preferences. As the anime market expands, discovering suitable titles has become challenging. The research introduces a hybrid recommendation system that leverages emotion analysis from user reviews, using the DistilBERT base model for emotion classification and incorporating this data into a matrix factorization-based algorithm. Five algorithms were developed and compared. By incorporating emotional information, the recommendation precision improved, highlighting the significance of emotional data. The emotion dictionary-based model effectively captures users' emotional preferences, indicating successful integration of emotional data. The findings demonstrate that emotion analysis significantly enhances recommendation accuracy, although further refinement is needed in how emotional information is utilized within the system.

本研究では，ユーザのレビューテキストに着目し，レビューテキストから自然言語処理技術により抽出した情報を考慮した推薦システムの構築を行う．特にレビューテキストに含まれる感情情報に焦点を当てる．感情は，メタデータからは抽出できないユーザの潜在的な特徴を反映し，アニメの推薦システムにおける多様性や新しい選択肢を提供する上で重要な要素であると考えられる．また，非計画的な時間消費や購買が感情体験によって促進されることが明らかになっており，感情はユーザの意思決定の際に重要な役割を果たすことが示されている．そのため，感情情報を考慮した推薦により，ユーザにとって意外性のある発見や新しい体験を生み出すことができると考えた．本研究の目的は，ユーザレビューから抽出した感情情報を推薦アルゴリズムに組み込むことにより，個々のユーザの好みやニーズに合致し，かつ感情が考慮されたアニメの推薦を行うことである．


Constructing multimodal counseling dataset for depression detection and feature analysis

マルチモーダルなカウンセリングデータセットの構築と特徴量の分析

ITヘルスケア誌 第17回年次学術大会抄録集

Japan Association of Applied IT Healthcare

In recent years, depression caused by stress and anxiety has been on the rise worldwide, and the number of people taking leave due to depression and other mental illnesses has been increasing in Japan as well. However, there is a serious shortage of counselors and psychiatrists, making early detection difficult. In this study, for early detection of depression, 25 Japanese university students were interviewed by a professional counselor for about 30 minutes, and a multimodal dataset consisting of verbal, audio, video, and heart rate data was constructed during the interview. Before and after the interviews, a questionnaire about the subjects' current psychological state was also administered. The constructed dataset was analyzed by extracting features using GiNZA, OpenSMILE, and OpenFace, and comparing them with the results of the questionnaire. In the future, we plan to conduct interviews with mentally ill subjects and collect data from them.

近年，ストレスや不安を原因とするうつ病は世界的に増加傾向にあり，日本でもうつ病などの精神疾患による休職者が増加している．しかし，カウンセラーや精神科医の不足は深刻で，早期発見が困難な状況にある．本研究では，うつ病の早期発見のため，日本の大学生25名を対象に，専門のカウンセラーによる約30分間の面接を行い，面接中の言語，音声，映像，心拍数データからなるマルチモーダルデータセットを構築した．面接の前後には，被験者の現在の心理状態に関する質問票も実施した．構築したデータセットは，GiNZA，OpenSMILE，OpenFaceを用いて特徴量を抽出し，アンケート結果と比較することで分析を行った．今後は，精神疾患者へのインタビュー調査を実施し，データを収集する予定である．


A Citation Selection Method Based on Literature Feature Analysis to Support Writing Review Papers

レビュー論文執筆支援に向けた文献特徴分析に基づく引用文献選択手法

Proceedings of the 38th Annual Conference of the Japanese Society for Artificial Intelligence

第38回人工知能学会全国大会予稿集

The Japanese Society for Artificial Intelligence

This paper proposes a semi-automatic, efficient, and accurate method for selecting citations for systematic reviews. As a feature extraction method, we used similar sentence search to extract important descriptions and expressions that serve as citation criteria for systematic review articles, but some issues remain. We will examine the algorithm for citation selection by reviewing and narrowing down the methods of literature collection and constructing a classification model for selected and excluded papers in the systematic review.

この論文では，系統的レビュー(Systematic Review)に用いる引用文献を選択するための，半自動による効率的かつ高精度な方法を提案する．特徴抽出手法としては，類似文検索を用いて系統的レビュー論文の引用基準となる重要な記述や表現を抽出するが，いくつかの課題が残されている．文献収集方法の見直しと絞り込み，システマティックレビューにおける選定論文と除外論文の分類モデルの構築により，引用選定のアルゴリズムを検討する．


Improvement of health maintenance support system using a conversation robot by adding chat functions

雑談機能の追加による対話ロボットを用いた健康維持支援システムの改良

第38回人工知能学会全国大会予稿集

We are developing a system that helps people improve their lifestyles by using an interactive robot and a web application that analyzes the content of the dialogue and suggests lifestyle habits. Using this system, we actually collected and analyzed lifestyle habits and evaluated the system, analyzing problems in the functionality and UI. Based on the results of this analysis, we aim to improve the system to make it more effective. This paper mainly describes the addition of a chat dialogue function and improvements to the analysis of user responses. The aim is to arouse the users interest in the system through the use of chat dialogues, to encourage continuous use of the system, and to motivate the user to improve his or her lifestyle. In addition, we confirmed that the use of GPT-3.5 in the analysis of user responses improves the accuracy of the Yes/No judgment of user responses.

対話型ロボットを用いて対話内容を解析し，生活習慣改善を提案するWebアプリケーションを活用し，生活習慣の改善を支援するシステムの開発を行っている．このシステムを用いて実際に生活習慣を収集・分析し，機能やUIの課題を分析してシステムの評価を行った．分析結果をもとに，より効果的なシステムへの改善を目指す．本稿では主に，チャット対話機能の追加とユーザー応答分析の改善について説明する．チャットによる対話を通じてユーザーにシステムへの興味を喚起し，システムの継続利用を促し，生活習慣の改善への動機付けを図るのが狙いである．また，GPT-3.5を利用したユーザー回答の分析により，ユーザー回答のYes/No判定の精度が向上することを確認した．


A Study on Improving Accuracy by Eliminating bias in Closed-domain Question Answering

クローズドメイン質問応答におけるバイアスの除去による精度向上に関する研究

電気学会全国大会講演論文集

The Institute of Electrical Engineers of Japan

質問応答の分野においてクローズドメイン質問応答というタスクでは与えられたコンテキストから回答箇所を推測する．しかし，各データセットにおいて偏りが生じてしまう．本研究では，クローズドメイン質問応答システムの精度と汎用性を向上させるため，複数のデータセットの組み合わせとアンサンブル学習による効果の比較実験を行う．この目的のために，3つの異なるデータセットを使用し，事前学習モデルとしてBERTを採用する．


Music Retrieval System by Inputting Arbitrary Words Using Impression Space Mapping

印象空間マッピングを用いた任意の語の入力による楽曲検索システム

2024年電子情報通信学会総合大会講演論文集

2024年電子情報通信学会総合大会講演論文集

Institute of Electronics, Information and Communication Engineers

近年，インターネット上のサービスによる音楽の聴取がCDなどに比べて主流になっており，このようなサービスではパーソナライゼーションによる感性に基づいた楽曲推薦などが行われている．しかし，土橋[1]は『「自分の嗜好」を強く反映してしまうパーソナライゼーション』(土橋 2011:35)は情報選択の範囲を狭めることにもつながると指摘している． 本研究では，楽曲探索をより広い範囲で行い，潜在的に好みの楽曲を検索するために，任意の語，すなわち自由に入力できるキーワードが表現する感性に基づいた楽曲検索システムの構築を目指す．


Interview Dialogue System Introducing Praise

褒めを導入したインタビュー対話システム

電気学会全国大会講演論文集

近年，IT技術の進化に伴って対話型エージェントが普及しており，人間とAIのコミュニケーションが一般的になってきた．将来は，高齢者施設や病院で生活している人，独居生活をしている人などの話し相手として活躍することもあるだろう．このようなシチュエーションで，対話型エージェントが人に愛されるために，ユーザに親しみを持って対話してもらえることが期待されている．本研究では，ユーザの発話を褒めるインタビュー対話システムの構築を目指す．提案手法では，ユーザの入力した発話文テキストから褒める対象となる単語の抽出を係り受け解析により行い，テンプレート文に当てはめたものを入力としてBERTを用いたマスク単語の予測により褒め対象単語の選定を行う．予測された褒め対象単語を当てはめ，BERTに入力することにより出力された褒め単語の中から，より具体的な褒め表現を選択することで褒める文章を完成させる．アンケートによる評価の結果，提案手法により生成した褒め文章において概ね高評価を得ることができた．入力と出力に用いたテンプレート文の差異により生成される文章が不自然になる問題があったため，今後は同じテンプレート文を使用することで課題を解決したい．


A Method for Survey on Studies of Paternal Postpartum Depression Using Topic Modeling and Large Language Model

トピックモデリングと大規模言語モデルを用いた男性の産後うつ関連研究調査手法

Shikoku-Section Joint Convention Record of the Institutes of Electrical and Related Engineers

電気・電子・情報関係学会四国支部連合大会講演論文集

男性(父方)産後うつの課題解決に役立つ研究を概観するために，男性の産後うつに関連する論文を対象とした研究動向の調査が必須である．しかし，膨大な文献テキストから必要な情報を人手により取得し，把握することは困難である．本研究では，専門的な知識を有さない人でも瞬時かつ高精度に，取得したい文献情報にアクセスできる関連研究調査システムの構築を目指し，潜在ディリクレ配分法(Latent Dirichlet Allocation;LDA)と，大規模言語モデル(Large Language Model;LLM)を用いた研究動向抽出手法を提案する．


Segmentation of Dental Panoramic X-ray Images Using the Deep Learning Model TransUNet

深層学習モデル TransUNet を用いた歯科パノラマ X 線画像のセグメンテーション

第22回情報科学技術フォーラム (FIT2023)

第22回情報科学技術フォーラム (FIT2023)

The Institute of Electronics, Information and Communication Engineers and Information Processing Society of Japan

歯科パノラマ X 線画像は歯の診療に使われる画像である．歯科パノラマの照合には専門的な知識が必要であり，歯科医師の負担軽減を目的として歯の自動診断システムの研究が行われてきた．その中で，深層学習技術を用いて歯の識別を行う状態分類モデルが開発されている．本研究では状態分類モデルの入力として必要な歯の輪郭情報を作成するため，深層学習技術を用いて歯科パノラマ X 線画像のセグメンテーションを行う．実験では，歯科クリニックで撮影，ラベル付 けされたデータを取り扱う．セグメンテーションモデルには，近年医療セグメンテーションタスクで成功を収めているTransUNet モデルを用いて実験を行った．


Automatic Classification Method for Dental Implants Using Deep Segmentation Model

深層セグメンテーションモデルによる歯科インプラントの自動分類手法

第22回情報科学技術フォーラム (FIT2023)

第22回情報科学技術フォーラム (FIT2023)

歯科インプラントとは，失った歯を補うための医療機器である．インプラントは，メーカーや種類が様々あり，それぞれで治療法が異なるため，歯科医師は治療時にインプラントの種類を把握しておく必要がある．本研究では，歯科医師の負担軽減を目的として，患者の歯科デンタル X 線画像から深層学習を用いたセグメンテーションモデルによってインプラントの領域を切り取り，画像分類モデルを用いてインプラントの分類を行う．切り抜きの精度は，IoU と Dice 係数でそれぞれ，0.923，0.857 となった．U-Net によってインプラントの領域を切り抜いた画像に対する分類の評価は，正解率，適合率，再現率，F 値がそれぞれ，0.851，0.852，0.884，0.840 となった．


Diagnosis of Teeth Condition in Dental Panoramic X-Ray Images and Validation of Generalization Performance

歯科パノラマX線画像における歯の状態診断及び汎化性能の検証

第22回情報科学技術フォーラム (FIT2023)

第22回情報科学技術フォーラム (FIT2023)

近年, 医療ビッグデータの深層学習技術への活用が盛んに行われており, 医療ビッグデータから診療情報や画像, カルテ情報等を用いて診断結果の提示や診断援助等ができるようになってきた. 本研究では, 歯科医師の負担軽減と診断援助を目的として, 深層ニューラルネットワークアーキテクチャを利用した歯科パノラマ X 線画像における歯の状態診断を行う. 実験では, 共同研究先の歯科クリニックで撮影, ラベル付けされたデータを取り扱う. 歯は複数の状態を持つため, 分類モデルはマルチラベル分類手法を用いて実装する. 結果として, う蝕や根尖病巣の識別が困難であった. また, 未知の撮影機材由来のデータでは精度の低下が見受けられた. そのため, ドメイン汎化手法を用いて, さらなる検証を行った.


Correction of inappropriate sentences using toxic sentence judging

攻撃的文章判定を用いた不適切文章の訂正

第22回情報科学技術フォーラム (FIT2023)

第22回情報科学技術フォーラム (FIT2023)

近年, SNS 利用者が急増しており, 8 割以上の人が使用しているという統計データもある. これに伴い, SNS 上で誹謗中傷や炎上などの被害に遭う件数も増加している.2020 年には某番組内でタレントがとった行動や言動への Twitter 上での誹謗中傷が, 結果的に自殺を幇助してしまった事例もある. 2022 年には回転寿司店での不適切行為に対して不特定多数の人たちが非難を浴びせる「私刑」と呼ばれる現象も問題となった.このような事件を回避する方法として我々は, 攻撃的な内容を含んだ文章を投稿前に検出し, 適切な文章に訂正するシステムを提案する.


Investigation on Accuracy Improvement of Emotion Classification Based on Text Data

テキストデータ拡張による感情分類精度向上の検討

第37回人工知能学会全国大会講演論文集

第37回人工知能学会全国大会講演論文集

The Japanese Society for Artificial Intelligence

Recently, SNSs have facilitated the collection of a wide variety of text data. However, SNS text data has problems such as short sentences with abbreviations and colloquial expressions, which make labeling difficult, and the difficulty of collecting a large amount of data in a short period of time. To solve this problem, data expansion is an effective method for efficiently preparing large-scale, high-quality labeled text data for machine learning. In this research, we aim to improve the learning accuracy of sentiment classification by extending the data to Japanese texts. EDA (Easy Data Augmentation) was used as the data expansion method. In particular, the use of various models for text manipulation in the EDA increased the range of data expansion. The expanded text generated by data expansion was evaluated based on the semantic similarity and the degree of textual change. The optimal data for training was selected by determining a threshold value. The WRIME corpus was used as the dataset to ensure the reliability of the labels. In this presentation, we report the results of learning accuracy in sentiment classification using data expansion.


コロナ禍前後におけるTwitterユーザの性格別感情変化の分析

コロナ禍前後におけるTwitterユーザの性格別感情変化の分析

言語処理学会 第29回年次大会 発表論文集

言語処理学会 第29回年次大会 発表論文集

The Association for Natural Language Processing

Three years have passed since covid-19 pandemic began at the end of 2019, and the situation is changing daily as restrictions on behavior are gradually being eased. Even now, the dramatic changes in lifestyles continue to have a significant impact on people in terms of physical and mental stress. Our study analyzes the changes that occurred in people's statements, thoughts, and actions by conducting emotion analysis of the content of statements made on Twitter before and after corona virus pandemic, extracting characteristic expressions, and conducting a comparative analysis. In addition, we considered the changes in emotions by user personality type.

2019 年末から始まったコロナ禍も 3 年が経過し，徐々に行動制限が緩和され，日々，状況は変化しつつある．現在も，生活様式の劇的な変化に伴い心身へのストレスなど人々に大きな影響を与え続けている．本研究は，コロナ禍前後での Twitter 上での発言内容から感情分析を行い，特徴的な表現を抽出して比較分析することにより，人々の発言，思考，行動に起きた変化を分析する．また，ユーザの性格タイプ別での感情の変動について考察する．


Development of health maintenance support system using spoken dialogue robots

音声対話ロボットを用いた健康維持支援システムの開発

HAI シンポジウム2023 講演論文集

HAI シンポジウム2023 講演論文集

Lifestyle-related diseases account for 30% of all medical costs and 60% of all deaths. It is important to maintain good lifestyle habits to protect one's health. In recent years, an increasing number of services and devices allow voice input. Speech recognition makes it easier to use computers. In our study, we develop a system that helps people improve their lifestyle habits by using a web application that analyzes the content of conversations with an interactive robot and suggests lifestyle habits and other information. Using the developed system, we actually collected lifestyle habits, analyzed them, and evaluated the system.

生活習慣病は医療費全体の3 割，死亡者の6 割を占めている．よい生活習慣を送ることが健康を守るうえで重要である．また，近年音声で入力できるサービスや機器が増えてきている．音声認識を使えば，より手軽に計算機を扱えるようになる．本研究では，対話ロボットとの対話内容を分析して生活習慣などを提示するWeb アプリケーションを用いて，生活習慣の改善を助けるシステムを開発する．開発したシステムを用いて実際に生活習慣を収集し，生活習慣の分析とシステムの評価を行った．


Highlight Extraction in Stories Considering Character's Emotional Movements

登場人物の感情の動きを考慮した物語におけるハイライト文抽出

HCGシンポジウム2022発表予稿集

HCGシンポジウム2022発表予稿集

一般社団法人電子情報通信学会

一般社団法人電子情報通信学会

In this study, we propose a method for extracting and summarizing important sentences in novels by quantifying the magnitude and changes of characters' emotions. We selected five novels from the Aozora Bunko as research subjects and performed automatic summarization of each novel using the proposed method. We created correct answer data that was manually extracted sentences that express characters' emotions and evaluated the proposed method by precision and average precision of the top 10 sentences

本研究では，小説における登場人物の感情の大きさや変化を数値化することによる重要文の抽出要約手法を提案する．研究対象として青空文庫から5作品の小説を選び，それぞれの小説に提案手法による自動要約を行った．評価に関しては，人手で抽出した登場人物の感情が大きく表れている個所を正解データとし，上位 10 文における適合率と平均適合率を用いて評価を行った．


Development of dialogue model reflecting individuality based on interview dialogue

インタビュー対話に基づく個性を反映した対話モデルの構築

令和4年度 電気・電子・情報関係学会 四国支部連合大会講演論文集

令和4年度 電気・電子・情報関係学会 四国支部連合大会講演論文集

人間が AI とより親しみのあるコミュニケーションをするための要素の一つとして「個性」が挙げられる．AI に必要とされる個性は場面や状況に応じて異なるので，対話する相手に応じて個性を切り替えられるような仕組みが求められる．本研究では，日本語Transformer encoder-decoder対話モデルを使用し，インタビュー対話において異なる個性を持つ対話データごとにファインチューニングすることで対話モデルの個性に違いや特徴が出るかどうかを評価することを目的とする．


感情辞書を用いた物語のハイライト抽出

感情辞書を用いた物語のハイライト抽出

第19回テキストアナリティクス・シンポジウム

第19回テキストアナリティクス・シンポジウム


Affective analysis and visualization from posted text, replies, and images for analysis of buzz factors

流行要因分析のための投稿テキスト・リプライ・画像からの感性分析及び可視化

第21回情報科学技術フォーラム講演論文集(ハイブリッド開催)

第21回情報科学技術フォーラム講演論文集(ハイブリッド開催)

Institute of Electronics, Information and Communication Engineers

本研究ではTwitter上で起こるバズ現象について，ツイート及び添付された画像，ツイートに対して寄せられたリプライの感性を分析することにより探る．Twitter に投稿されるテキストの文字数には制限があるが，テキストのみで表現することが難しい情報には画像を添付することで視覚的に伝えやすくなる．また，バズ現象を分析するという観点からも，画像付きのツイートはテキストのみのツイートよりも分析に使える情報量が増えるため，より詳細な分析が可能になると考えられる．ツイートの事後情報であるリプライテキストも分析に用いることができる．リプライは，ツイート投稿後に，そのツイートに対する反応として投稿されるものであるため，具体的なバズ現象の要因分析につながると考えた．本研究では，画像付きツイートについて，ツイートテキスト，画像，リプライテキストからそれぞれ抽出した感性情報を分析することで，バズ現象の要因分析を検討する.


コメントデータと歌詞に基づく楽曲動画検索システム

コメントデータと歌詞に基づく楽曲動画検索システム

Proceedings of the 36th Annual Conference of the Japanese Society for Artificial Intelligence

2022年度 人工知能学会全国大会講演論文集

Currently, video sharing services such as YouTube are used by many people. In these services, keyword search is the mainstream. In the case of keyword search, it is difficult to find a video that matches the user's interest unless appropriate words are used. In this study, We propose a method to retrieve music videos with similar impressions by analyzing the comment data given by viewers to music videos on YouTube and the lyrics of the music. In the proposed method, comments and lyrics are converted into vectors by Word2Vec, and the music videos with similar impressions are retrieved based on clustering by Fuzzy c-means. According to the mean reciprocal rank (MRR) scores, it was clear that the output of the music videos had the same impression within the top 3 songs.

現在，YouTubeに代表される動画共有サービスが多くの人に利用されている．これらのサービスでは，キーワード検索が主流となっている．キーワード検索の場合，適切な単語を用いなければ，ユーザの興味に合った動画を探し出すことは困難である．そこで本研究では，YouTube上の音楽動画に対して視聴者がつけたコメントデータと歌詞を解析することで，印象の似た音楽動画を検索する手法を提案する．提案手法では，コメントと歌詞をWord2Vecによりベクトル化し，Fuzzy c-meansによるクラスタリングにより，印象の類似した音楽動画を検索する．平均相互順位(MRR)スコアによると，上位3曲の中で同じ印象を持つ音楽ビデオが出力されていることが明らかになった．


Color Image Scale Reconstruction Using Multivariate Analysis and Machine Learning

多変量解析と機械学習を用いたカラーイメージスケールの再構成に関する一検討

第17回日本感性工学会春季大会


SNS の投稿内容に基づく生活習慣病発症リスクの分析

SNS の投稿内容に基づく生活習慣病発症リスクの分析

言語処理学会 第28回年次大会 発表論文集

言語処理学会 第28回年次大会 発表論文集

国内における死亡原因は，がん・循環器疾患・糖尿病などで全体の約 6 割を占めている．これらの病気はいずれも食事・運動・睡眠などの生活習慣と深い関りがあり，生活習慣病と呼ばれている．生活習慣病には自覚症状がほとんどないため，病気の悪化に気づきにくいという特徴がある．本研究では，より簡易的に生活習慣病を予防するシステムの構築を目指し，ソーシャル・ネットワーキング・サービス(SNS)を用いて，ユーザの投稿内容から食事・運動・体調・精神状態に関するツイートの分析を行った．調査の結果，本研究の成果がユーザの投稿内容から生活習慣病発症の危険因子を検出するシステムの構築につながることが分かった．


登場人物の感情表現文の抽出による物語のハイライト生成

登場人物の感情表現文の抽出による物語のハイライト生成

電子情報通信学会2022年総合大会 情報・システムソサイエティ特別企画 ジュニア&学生ポスターセッション予稿集

電子書籍の普及により数多くの文学作品をインターネット上で読むことが可能になった．しかし，読者がそれらの中 から読む作品を選ぶことは容易ではない．本稿では物語について登場人物の感情が表現されている文を抽出することによる指示的要約の手法を提案する．


BERTを用いたSNS上における攻撃的文章訂正システム

BERTを用いたSNS上における攻撃的文章訂正システム

情報処理学会第84回全国大会講演論文集

情報処理学会第84回全国大会講演論文集

現代社会において SNS の発展は凄まじく，ほとんどの人が使用しているという状況に置かれている．それに伴い，誹謗中傷や炎上などの被害にあう件数も増加している．2020 年には，某テレビ番組における出演者の行動や言動に対して，Twitter 上で誹謗中傷が多数投稿されたことから，自殺に追い込んでしまうといった事件が起きている．本研究では，SNS ユーザが発言を投稿する前に，投稿文の攻撃性を判定し，攻撃性を緩和した文章に変換して提示するシステムの構築を目指す．


投稿文および画像といいね数との関係に基づく流行要因分析

投稿文および画像といいね数との関係に基づく流行要因分析

本研究ではソーシャルメディアの動向を調査し，Twitterでのバズ現象を支える要因を探るための分析手法を提案することを目的とする．Twitterに投稿されたテキスト内容だけではバズ現象の原因を特定できない場合があるため，画像が添付されたツイートに限定し，テキストと画像のそれぞれの特徴に関連性があるかどうか，また，これらの特徴の関連性が，人気の指標である「いいね」や拡散の規模を表す「RT」の数とどのように関連するかを調べた．画像とテキストから抽出した特徴量を入力とし，いいね数とRT数を出力した後，2つの入力(画像とテキスト)から同じ次元数の特徴ベクトルを中間層から抽出するためにマルチタスク学習型のニューラルネットワークを学習する．これらの特徴ベクトル間の距離といいね数との関係を分析した結果，BERTによる分散表現とInceptionResNetV2の特徴量がいいね数とRT数を予測する要因となり得ることがわかった．


Lifestyle Extraction from Corpus of Disease Fighting Blogs

闘病ブログコーパスからの生活習慣の抽出

Proceedings of 2021 Shikoku-Section Joint Convention Record of the Institutes of Electrical, Electronic, and Information Related Engineers

令和3年度 電気・電子・情報関係学会 四国支部連合大会 講演論文集

令和3年度 電気・電子・情報関係学会四国支部連合大会

令和3年度 電気・電子・情報関係学会四国支部連合大会

闘病ブログは，患者の生活に関する行動情報(病状・食事・運動)について書かれているものがあり，疾患への理解や納得を得られるものも見られ，同病者の励 みとなっている．闘病ブログコーパスとは，闘病ブログを収集し，記事のタグ付けを行ったものである．本研究では年々増加傾向にある2 型糖尿病患者による闘病ブログコーパスから，闘病活動に関わる生活習慣の分析を行う．この分析結果より，病状悪化の危険因子や病状改善の手がかりとなる要素を，非医療従事者が自然言語処理および機械学習手法を用いて発見し，闘病者や糖尿病予備軍の人々に助言できるシステムの構築を目指す．


Buzz tweet classification based on texts and images

発言内容と画像に基づくバズツイート分類

JSKE 第23回日本感性工学会講演論文集

JSKE 第23回日本感性工学会講演論文集

As the information diffusion on social networking sites has become more common, content buzzes have become a frequent occurrence on the Internet via social networking sites. In marketing, it is important to know what is trending, and it is necessary to have a means of quickly detecting buzz on SNS. In this study, we define buzz tweets based on the number of likes and RTs on a tweet, and analyze the content of the tweets to obtain the characteristics unique to buzz tweets, so that we can predict buzz tweets in advance. In this study, we propose a classification method to predict whether a tweet is a buzz tweet or not, based on the statement of the tweet and the attached image. The results of the classification experiments show that the classification accuracy is higher when the statement of the tweet and the image are used as features than when only the content of the tweet and the image are used as features, indicating the effectiveness of the proposed method.


Extraction of Emotional Information from Distributed Representation Using Emotion Classification

感情分類を用いた単語分散表現からの感性情報抽出

第20回情報科学技術フォーラム(FIT2021)講演論文集

第20回情報科学技術フォーラム(FIT2021)講演論文集

近年，自然言語処理の分野では単語の一般的ではない用法の検出など，単語分散表現から意味推定を行う技術が研究されている．それらの研究では分散表現全体から単語の情報を抽出しており，感性以外の情報が多く含まれている．本研究では感性情報のみを抽出することでその精度上昇を目指す．その手法として単語分散表現から感情分類を行うニューラルネットワークを作成し，ネットワークが学習した重みから感性情報が強く表れている次元の情報を抽出する．評価方法としては，コサイン類似度を用いて抽出次元同士が類似した単語の比較を行う.


情報処理学会 第83回全国大会

情報処理学会 第83回全国大会


Effectiveness of Emotional Information in Construction of MBTI Personality Estimation Model

MBTI性格推定モデルの構築における感性情報の有効性

日本感性工学会春季大会(JSAEE2021)講演論文集

日本感性工学会春季大会(JSAEE2021)講演論文集

In recent year, anyone has become possible to interact with many people all over the world easily because of that SocialNetworking Services (SNS) such as Twitter and Instagram have become widespread. Many researchers are analyzing user's personalityand emotion using features such as text information and image information of SNS. Our study analyzes the effectiveness of usingemotional information as a feature when constructing a personality estimation model for Twitter users. For analysis, we use MBTI(Myers-Briggs Type Indicator), one of the widely used personality theories for personality indicators.

本研究は性格推定システムの精度向上を図るため分類モデルの学習時の特徴量としてツイート文から抽出した文ベクトルだけではなく，ツイート文から推定した感性情報も特徴量として利用する．ツイート文から感情推定モデルをもとに感性情報ベクトルを取得し新たな特徴として用いることを試みる．性格推定において，ツイート文から抽出した文ベクトルを用いて性格推定モデルを学習させた場合と，文ベクトルと感性情報ベクトルを複合させたものを特徴量として学習させた場合の結果を比較分析し，Bag of WordsとSVDに基づく従来手法と比較し，性格推定モデルの構築の際に感性情報が有効であることを明らかにした．


Tooth detection and segmentation from panoramic radiographs using Mask R-CNN

Mask R-CNNを用いたパントモ画像からの 歯牙検出及びセグメンテーション

令和2年度電気・電子・情報関係学会四国支部連合大会

令和2年度電気・電子・情報関係学会四国支部連合大会

Institute of Electronics, Information and Communication Engineers

In Japan, the number of diagnostic items in dental diagnosis is considerably larger than in other countries, and it is necessary to reduce the burden of diagnosis on dentists because it is time-consuming for dentists to write dental formulae from scratch. Since interpretation of pantomime images is essential for diagnosis, understanding of dental health status, and planning of treatment, processing of pantomime images is an important issue for automation. In this study, tooth detection and segmentation from pantomime images are performed automatically. In this paper, we describe the method.

歯科診断における日本の診断項目数は海外と比べてかなり多く，歯科医師が 1 から歯式を書くこ とは手間がかかるため，歯科医師の診断負担軽減 が必要である．診断，歯の健康状態の把握，治療 計画の立案にはまずパントモ画像の解釈が不可欠 であることから，パントモ画像の処理は自動化の 重要な課題である． 本研究では自動でパントモ画像から歯牙検出・ セグメンテーションを行う．本稿では，その方法 について述べる．


A Study on the Analysis of Comics Using Deep Learning

深層学習を用いたコミックの分析に関する研究

令和2年度電気・電子・情報関係学会四国支部連合大会 講演論文集

令和2年度電気・電子・情報関係学会四国支部連合大会 講演論文集

The way we enjoy manga, a Japanese subculture, has been changing in recent years. In recent years, the way we enjoy manga, a Japanese subculture, has been changing. Looking at the sales trends in the market, we can see that e-books, which are read on electronic devices instead of paper books, are becoming popular. However, while smartphones and other small-screen devices are highly portable and easy to use, they are too small to display comic books, which are usually in paperback or A5 format, page by page. However, it is too small to display comics, which are usually in paperback or A5 size, page by page. If it is possible to extract and rearrange frames, it may provide an optimal reading environment for readers. In this study, we investigate the detection of coma regions to solve this problem.

日本のサブカルチャーである漫画の楽しみ方が近年変化している．市場の売り上げ推移を見ると 紙の書籍ではなく電子端末で閲覧する電子書籍が 普及しつつあることがわかる．しかしスマートフ ォンを始めとする画面の小さい端末では携帯性が 高く扱いやすい反面，通常文庫判∼A5 判である漫 画をページ単位で表示するには小さすぎる． コマの抽出及び並び替えが可能であるなら読者にとって最適な読書環境を提供できる可能性があ る．そこで，本研究ではこの問題を解決するべくコ マ領域の検出について検証する．


ASCII Art Category Classification Based on Transfer Learning and Data Augmentation from Different Types of Image Corpus

異なるタイプの画像コーパスからの転移学習とデータ拡張に基づくアスキーアートのカテゴリ分類

JSKE 第22回日本感性工学会大会講演論文集

JSKE 第22回日本感性工学会大会講演論文集

In this paper, we propose an ASCII art category classification method based on transfer learning and data augmentation.ASCII art is one of the nonverbal expressions that visually express emotions and intentions.There are similar expressions such as emoticons and pictograms. However, most of them are either represented by a single characteror embedded in the statement as an inline expression. ASCII art is expressed in various styles, including dot art illustration and lineart illustration. Basically, ASCII art can represent almost any object, therefore the category of ASCII art is very diverse. Many existing image classification algorithms use color information, however, since most ASCII art is written in character sets, there is no color information available for categorization. Create an ASCII art category classifier using the grayscale edge image and the ASCII art image transformed from the image as a training image set. We also use VGG16, ResNet-50, Inception v3 and Xception's pretrained networks to fine-tune our categorization. As a result of the experiment of fine tuning by VGG16 and data augmentation, an accuracy of 80% or more was obtained in the "human" category.


テレビ番組視聴者のツイートからのキーワード抽出および分類

テレビ番組視聴者のツイートからのキーワード抽出および分類

2020年度人工知能学会全国大会(第34回)

2020年度人工知能学会全国大会(第34回)

The Japanese Society for Artificial Intelligence

近年，若者を中心にテレビ離れが進んでいる． その主な理由としては，長時間番組であったり，視聴者の視聴時間が限られているためである．また，近年，ソーシャルメディアの発達に伴い，テレビ番組の視聴において，リアルタイムに意見や感想を述べる機会が増加した．実際にTwitter上では，いわゆる実況ツイートが盛んに行われている．そこで，本研究では，テレビ番組実況ツイートを用いてテレビ番組のイベントシーンに関するキーフレーズを抽出する手法を提案し，効率的なテレビ番組視聴を目指したシーン検索システムを提案する．実験の結果，番組時間に対して約5%∼10%の誤差で番組内容を推定した．また，抽出されたキーフレーズをt-SNEアルゴリズムによってイベントシーンカテゴリごとに視覚化した．


ツイート内容による夜間活動休止時間の推定に関する研究

ツイート内容による夜間活動休止時間の推定に関する研究

情報処理学会第82回全国大会講演論文集

情報処理学会第82回全国大会講演論文集


文書分散表現を用いた音楽アーティストの特徴分類に関する研究

文書分散表現を用いた音楽アーティストの特徴分類に関する研究

情報処理学会第82回全国大会講演論文集

情報処理学会第82回全国大会講演論文集


Analysis of Utterance Intention and Kansei on Interview Dialogue Corpus

インタビュー対話コーパスにおける発話の意図および感性の分析

In this study, we analyze interview dialogue corpora taken from an interview-type TV program, aiming at realizing aninterview dialogue system that brings out the best of the users of the system. After defining a tag set that classifies intention of utterance,these tags were added to six types of interview dialogue corpora, then we conducted utterance intention estimation using utterancesentence vectors obtained by using a pre-trained model. In the experiments, we analyze the expressions that are strongly related to theutterance intention, other important expressions, and the tendency of utterances of both the interviewers and the interviewees.


深層ニューラルネットワークに基づくテキストと画像間のクロスメディア検索

深層ニューラルネットワークに基づくテキストと画像間のクロスメディア検索

電子情報通信学会2020年総合大会学生ポスターセッション

電子情報通信学会2020年総合大会学生ポスターセッション


深層学習を用いたパントモ画像からのインプラント体検出

深層学習を用いたパントモ画像からのインプラント体検出

電子情報通信学会2020年総合大会学生ポスターセッション

電子情報通信学会2020年総合大会学生ポスターセッション


第21回日本感性工学会大会

第21回日本感性工学会大会


深層学習に基づく配色イメージの感性マッピング

深層学習に基づく配色イメージの感性マッピング

第18回情報科学技術フォーラム (FIT2019)

第18回情報科学技術フォーラム (FIT2019)


Word Mover's Distanceを用いたコーパス拡張による感情推定精度向上の検討

言語処理学会第25回年次大会(NLP2019)

言語処理学会第25回年次大会(NLP2019)

The Association for Natural Language Processing

近年，深層学習をはじめとする機械学習はテキストマイニングや画像認識などの様々な研究分野で利用されている．今後の技術発展において必要不可欠であり，莫大な費用を投じている企業も少なくない．また，感情推定技術は人間とコンピュータ同士の円滑なコミュ ニケーションの実現に重要な役割を果たす．そういった中，よく問題点として挙げられるのが訓練データ量の不足や質の低下である．データ量の不足，質の低下は機械学習そのものの質の低下に直結する．本研究では，代表的な SNS の一つである Twitter に投稿され るテキストである「ツイート文」に着目し，そこに投稿されるツイート文を感情推定の対象とする．また，コーパス拡張が感情推定モデルの精度にどのような影響を及ぼすかを分析し考察する．


リプライを用いたバズツイートの分類

リプライを用いたバズツイートの分類

言語処理学会第25回年次大会(NLP2019)

言語処理学会第25回年次大会(NLP2019)

近年 SNS の発達により，多数のユーザ間で口コミの拡散や共有が盛んにおこなわれるようになった．これによりインターネット上でコンテンツの流行が度々起こるようになった．ユーザ同士のコミュニケーションによっておこる流行のことを「バズる」と呼ぶ．この「バズる」ことを利用したマーケティングが重要視されており，いち早くバズに気づき取り入れることが今後のマーケティングで重要になってくると考えられる．既存研究において，時系列での拡散のされ方などから流行を検知するものがあるが，それらの手法 では拡散された要因までは知ることができないという問題点がある．本研究では，Twitter においてバズったツイートに寄せられたリプライをベクトル化し，ツイートに対するリプライ全体を特徴ベクトルとすることで，「バズっている」特徴の定義を目指すとともにバズツイート内におけるタイプ別の分類分けの可能性について検討する．


IDRユーザフォーラム 2018

IDRユーザフォーラム 2018


Classification of ASCII arts by Deep Convolutional Neural Networks

深層畳み込みニューラルネットワークによるアスキーアートの分類

平成30 年度電気関係学会四国支部連合大会 講演論文集

平成30 年度電気関係学会四国支部連合大会 講演論文集

近年のSNS でのテキストコミュニケーションにおいて，顔文字，絵文字，スタンプといった非言語表現は感情伝達において重要であり，ショートテキストで円滑なコミュニケーションを行う際には欠かせないものとなっている．非言語表現のうち，アスキーアート(ASCII Art; 以下AA)は使用されている文字が特に意味を持たないことが多く，その配置や形状により視覚的に感情や意図を伝達，表現できる．しかし，それ故に使用されている文字を手がかりとした分類が難しい．本研究では，AA を画像化し深層学習を用いて分類する手法を提案する．


Comparison of Classification Methods for Ascii Art

アスキーアート分類手法の比較検討

第32回人工知能学会全国大会講演論文集

第32回人工知能学会全国大会講演論文集

In recent years, a lot of non-verbal expressions have been used on social media. Ascii art (AA) is an expressionby visual technique using characters. In this paper, we set up an experiment to classify AA pictures by usingcharacter features and image features. We try to clear which feature is more effective for the method to classify AApictures. We proposed four methods; 1) character frequency based method, 2) character importance value basedmethod and 3) image feature based method, 4) characters image feature based method. We trained the neuralnetworks by using these four features. As the experimental result, the best classification accuracy was obtainedwith the feed forward neural networks using characters image feature.

近年，ソーシャルメディアにおいて数多くの非言語表現が使用されている．そのなかでも，アスキーアート(AA)は，文字を用いた視覚的な技法による表現の一つである．本論文では，文字特徴と画像特徴によるアスキーアート分類手法を比較検討し，アスキーアートのカテゴリ分類に効果的な手法を評価実験により明らかにする．評価実験では，1) 文字頻度，2) 文字重要度，3) 画像特徴量，4) 文字の画像特徴量 の4種類を比較する．この4つの特徴量を用いて，ニューラルネットワークの学習によりカテゴリ分類器を作成する．実験の結果，文字単位での画像特徴を用いて学習させたフィードフォーワードニューラルネットワークによる分類器が，全体的な精度が優れていることが分かった．


Emotion Recognition from Emoticons based on Deep Neural Netwotks

2018年電子情報通信学会総合大会ISS特別企画学生ポスターセッション予稿集

2018年電子情報通信学会総合大会ISS特別企画学生ポスターセッション予稿集

Institute of Electronics, Information and Communication Engineers

Text communication has been developed in recent years, all kinds of daily contacts are through the SNS regardless of age, gender and active. As an advantage of text-based communication via the Internet. There is a point that you can quickly respond to anyone wherever you are. In this paper, by letting the deep convolution neural network learn the image features of emoticons. We propose a method to classify by emotion expressed by emoticons. By treating emoticons as images, it is possible to capture visual features and think that natural identification can be made rather than handling as character strings.


絵文字を手がかりとした俗語感情コーパスの拡張

絵文字を手がかりとした俗語感情コーパスの拡張

言語処理学会 第24回年次大会 発表論文集

言語処理学会 第24回年次大会 発表論文集

絵文字が含まれていない文からでも，絵文字を推定することができれば，感情推定が可能になると考える．本論文では，俗語と絵文字を両方含む多量のコーパスを文字レベルで学習させ，絵文字推定器を作成し，俗語を含んだ発話文コーパスへの感情タグ付け手法を提案する．


プロフィール情報を用いたテキストの視点分類

プロフィール情報を用いたテキストの視点分類

同一トピックに関する Twitter への投稿を，視点によって分類する手法を提案する．提案手法は，ユーザーのプロフィール情報を援用し，各投稿内容の分散表現を学習することで，類似する視点からの投稿をまとめる．


Relation of Users Gender and Emotion Expression Tendency

ユーザの性別と感情表出傾向との関連

Proceedings of the 31st Annual Conference of the Japanese Society for Artificial Intelligence, 2017

第31回人工知能学会全国大会予稿集

The Japanese Society for Artificial Intelligence

In this study, we analyze the relation between emotion expressions of the Twitter users and their genders torealize emotion estimation adaptive to the users gender. We train a model to generate distributed representationsof sentences from a corpus divided according to gender. We train the features extracted with this model by using aneural network to construct an emotion estimator. The model constructed with our proposed method can estimateemotion with higher accuracy compared to the model without considering a gender. We also focus on slang andemoji as being related to emotion and analyzed their use tendencies to examine the difference between males andfemales.

感情表現や俗語表現の使用傾向は，発話者の性別の違いによって傾向があると考えられる．本研究では，性別に適応させた感情推定モデルの構築を行い，その有効性を検証する．さらに，提案モデルにより得られた感情表出傾向に基づき，口語文においてよく用いられる俗語表現の使用傾向が感情表出傾向とどのように関連があるかを，男女別で比較分析する．


Index Generation from BGM by Using the Distinctive Comments Posted on the Songs

楽曲に特徴的なコメントに基づく作業用BGMからのインデックス作成

第31回人工知能学会全国大会予稿集

The purpose of this study is to generate indexes to the BGMs based on the distinctive comments annotated tothe BGM videos posted on Nico Nico Videos. Our proposed method detects the end/start positions of the BGMsconsidering the increase/decrease of the number of the comment data, the comments distinctive to the songs andthe exclamatory expressions annotated to the videos. The evaluation result showed that the proposed methodcould generate correct indexes within 10 seconds error for the BGM videos with length of less than 40 minutes andwith annotation of over 30,000 comments.

作業用BGMとは，何らかの作業中に流す音楽のことである．本研究では，ニコニコ動画に投稿されている複数の楽曲が収録された作業用BGM動画を対象とし，楽曲ごとに固有の特徴的なコメントに基づく作業用BGMのインデックス作成を目的としている．提案手法では，動画に投稿されたコメントデータの増減と，楽曲固有の特徴的なコメントおよび感嘆表現(記号なども含む)に基づき，BGMの終了・開始位置を検出する．


Classification method for large ASCII arts using image features

画像特徴量を用いた大型アスキーアートの分類手法の提案

第31回人工知能学会全国大会予稿集

ASCII arts are visual expression that composed by strings. In the field of natural language processing, ASCII arts are treatedas a sequence of strings. In this paper, we consider to use shapes of ASCII art as features. Even if scale of ASCII arts and kindof characters that used to compose ASCII arts are different, proposed method enable to compare visual similarity. To extractshapes feature of ASCII art, we extract image features from ASCII art by converting ASCII arts to images. In the experiment,we compared visual similarity between ASCII arts and emoticons. As the experimental result, it was found that the imagefeature was effective to compare visual similarity even if the ASCII art and the emoticon used different kind of character

アスキーアート(AA)について，従来，顔文字のような小型のものを対象とした研究は多くあり，それら小型AAを単語として捉えた様々な抽出・分類手法が提案されてきた．しかし，複数行にわたる大型のAAも存在し，視覚的に類似しているAAであっても使用文字が異なるものが多数存在している．本研究ではAAを画像化し，画像特徴量を用いることで大型のAAに適した分類手法を提案する．


感情表出傾向の分析による対話破綻検出

感情表出傾向の分析による対話破綻検出

言語処理学会第23回年次大会発表論文集

言語処理学会第23回年次大会発表論文集

The Association for Natural Language Processing

本研究では，ユーザの感情の変化に合わせてシステム自身の感情を表出させることができないために起こる対話破綻について，発話文から推測可能な感情傾向の比較により検出する手法を提案する．具体的には，発話文の分散表現ベクトルをもとに感情が表出されている強度 を推定するモデルを用い，直前の 2 発話における推定結果との類似度の計算を行うことで，そのパターンを特徴量として対話破綻のラベルを推定する．評価実験では，提案手法を用いた対話破綻ラベルの推定をおこない，文の分散表現ベクトルの類似度パターンによる手法との比較をおこなう．


Visualization of the occurrence trend of infectious diseases using Twitter

Twitter を用いた感染症発生動向の視覚化

人工知能学会インタラクティブ情報アクセスと可視化マイニング研究会(第15回)

人工知能学会インタラクティブ情報アクセスと可視化マイニング研究会(第15回)

The purpose of this research is to extract comments on infectious diseases from Twitterand to create an infection map.As a method,we check the facts of the extracted utterance,extractthe past tweets of the infected person from the result,and estimate the place where the personlives.We used SVMs both for the fact check and place estimation.


Flame prediction based on harmful expression judgement using distributed representation

分散表現を用いた有害表現判別に基づく炎上予測

人工知能学会インタラクティブ情報アクセスと可視化マイニング研究会(第15回)

人工知能学会インタラクティブ情報アクセスと可視化マイニング研究会(第15回)

In recent years, flaming on social media has been a problem. To avoid flaming, it is useful for the system to automatically check the sentences whether they include the expressions that are likely to trigger flaming or not before posting messages. In this research, we target two harmful expressions. There are insulting expressions and the expressions that are likely to cause a quarrel. Firstly, we constructed a harmful expression dictionary. Because a large cost requires to collect the expressions manually, we constructed the dictionary semi-automatically by using word distributed representations. The proposed method used distributed representations of the harmful expressions and general expressions as features, and constructed a classier of harmful/general based on those features. An evaluation experiment found that the proposed method could extract harmful expressions with accuracy of approx. 70%. On the other hand, it was found that the proposed method could also extract unknown expressions, however, it wrongly extracted non-harmful expressions at a rate of approx. 40%.


表形式からの分散表現獲得

表形式からの分散表現獲得

情報処理学会自然言語処理研究会研究報告

情報処理学会自然言語処理研究会研究報告

表形式における単語埋め込み手法について考える．通常の文章を対象とした学習手法に比べ，表形式においては，単語の周辺文脈として，縦方向 ・ 横方向の二種類の文脈が考えられる．本稿では，これら同時に考慮した，二次元的なモデルを提案し，一次元的なモデルとの比較を行う．


8bit 音源に対する自動採譜システムに関する研究

8bit 音源に対する自動採譜システムに関する研究

平成28年度電気関係学会四国支部連合大会講演論文集

平成28年度電気関係学会四国支部連合大会講演論文集

本研究では，自動採譜分野において 8Bit 音源の特徴に着目して研究をする．8bit 音源とは，三角波と矩形波が二つの三和音とノイズからなる，一般的な音楽を形成するにあたる最低限の要素で構成された，シンプルな音源である． つまり，8bit 音源の採譜ができればベースやメロディーの動きが明らかになりその楽曲の骨組みが分かるようになる． 従来は，自動採譜の分野の研究において，一般的なクラシックやポップスなど，大量の楽音が混在した楽曲データを対象としていた．1)それに対し，本実験では 8-bit 音源を対象にし，その最低限の和音のみで構成されたものを対象にすることによる影響について考察した．


Anime Character Classification Using Face Image based on Deep Learning

深層学習に基づく顔画像によるアニメキャラクタの分類

平成28年度電気関係学会四国支部連合大会講演論文集

平成28年度電気関係学会四国支部連合大会講演論文集


Facial Expression Identification by Deep Convolutional Neural Networks

Deep Convolutional Neural Network による顔画像からの表情識別

第15回情報科学技術フォーラム講演論文集

第15回情報科学技術フォーラム講演論文集

Institute of Electronics, Information and Communication Engineers


An Illustration Image Classification By The Style Focusing on Infrequent Colors

出現頻度の低い色に注目した作風に基づくイラスト画像の分類

第15回情報科学技術フォーラム講演論文集

第15回情報科学技術フォーラム講演論文集


Twitterを用いた病気の事実性解析及び知識ベース構築

Twitterを用いた病気の事実性解析及び知識ベース構築

人工知能学会全国大会2016講演論文集

人工知能学会全国大会2016講演論文集


Twitterから抽出したユーザの発言に基づく健康状態の推定

Twitterから抽出したユーザの発言に基づく健康状態の推定

人工知能学会全国大会2016発表予稿集

人工知能学会全国大会2016発表予稿集


Personality Analysis Method of Twitter User for Information Recommendation

情報推薦のための Twitter ユーザの性格分析手法

The Proceedings of the 30th Annual Conference of the Japanese Society for Artificial Intelligence, 2016

人工知能学会全国大会2016発表予稿集

The Japanese Society for Artificial Intelligence


Detecting emoticons in ASCII arts using image features

画像特徴量を用いたアスキーアートからの顔文字検出

人工知能学会全国大会2016講演論文集


表形式のトピックモデルとその数値単位推定への応用

表形式のトピックモデルとその数値単位推定への応用

情報処理学会自然言語処理研究会研究報告

情報処理学会自然言語処理研究会研究報告

表形式中の数値表現について単位が省略されている場合に，その単位を推定するための手法を提案する．Wikipedia 表形式中の，数値のみを含むセルを対象に，一行目のセル等の 「周辺文脈」 を利用し，適切な単位を推定する．また，表形式の外側の文章を利用するために，表形式と文章を同時にモデル化するためのトピックモデルを提案し，トピックの推定結果を単位推定に利用することで精度の向上を図る．


Twitterユーザの属性別感情推定の検討

Twitterユーザの属性別感情推定の検討

言語処理学会第22回年次大会講演論文集

言語処理学会第22回年次大会講演論文集


深層畳み込みニューラルネットワークを用いた顔認識の精度評価

深層畳み込みニューラルネットワークを用いた顔認識の精度評価

2016年電子情報通信学会総合大会学生ポスターセッション

2016年電子情報通信学会総合大会学生ポスターセッション


トピック変動の分析による俗語の特徴抽出

トピック変動の分析による俗語の特徴抽出

本研究では俗語の使われ方が時間経過によって変化することに着目した．俗語の含まれたツイート文の集合を潜在的ディリクレ配分法により分析し時間ごとのトピックを得る．このトピックにおけるトピック重要語の集合について隣接する時間(月単位)での類似度を計算し，トピックの変化を抽出し，俗語の特徴となるものを検討する．


Shape-based image retrieval system for Two-dimensional Drawings

2次元図面の形状に基づく検索システム

Journal of Shikoku-Section Joint Convention of the Institutes of Electrical and Related Engineers

電気関係学会四国支部連合大会講演論文集


The optimal parameter estimation in the logo detection

ロゴ検出における最適パラメータ推定

電気関係学会四国支部連合大会講演論文集


A study on game music search by audio fingerprints

オーディオ指紋に基づくゲーム音楽の検索手法に関する研究

電気関係学会四国支部連合大会講演論文集


Sound Source Identification in Polyphonic Music Using a Mixed-sound Templates including Vocal Sound

ボーカル音を含んだ混合音テンプレートによる多重奏の音源同定

電気関係学会四国支部連合大会講演論文集


Cheating Detection by Using Correlation of Comment Distribution in Nicovideo

ニコニコ動画におけるコメント分布の相関に着目した不正動画検出

第14回情報科学技術フォーラム講演論文集

第14回情報科学技術フォーラム講演論文集