Enhancing Multimodal Tweet Analysis Accuracy through Integration of CLIP Model and Multi-layer Attention Mechanism

The 10th International Forum on Advanced Technologies 2024

In this study, we aim to conduct sentiment analysis and identify popular content on Twitter using a multimodal approach that combines the CLIP model with a multi-layer attention mechanism. By comprehensively analyzing both text and image information shared on Twitter, we can precisely grasp the trends in emotions and indicators of popularity. This enables various applications such as analysis of social media trends, evaluation of influencers' impact, and even prediction of market trends. In particular, identifying popular tweets based on the number of likes is expected to contribute to understanding the mechanisms of viral marketing and information dissemination.


Upper Limb Motor Velocity Compared Between Patient with Schizophrenia and Healthy Subject: A Case Study

6TH SPUP INTERNATIONAL INTERDISCIPLINARY RESEARCH CONFERENCE


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

Proeedings of the19th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery

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.


A pilot case study for developing a software for human emotion recognition using multimodal data

2023 IEEE/SICE International Symposium on System Integration (SII)


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.


Objective Analysis Techniques for Descriptive Answer Sentences in Questionnaires Using Natural Language Processing and Machine Learning

4TH INTERNATIONAL CONFERENCE ON TECHNOLOGICAL COMPETENCY AS CARING IN NURSING AND HEALTH SCIENCES 2022

When analyzing descriptive answer sentences in the questionnaire survey, there is the problem that the subjectivity of theanalyst may enter into the qualitative analysis results. For example, whether or not sentences corresponds to expressions suchas the spread of the new coronavirus infection as the cause of spending less time with family in a freestyle descriptiveanswer will vary depending on the skill and perspective of each analyst. Natural language processing technology and machinelearning algorithms enable objective analysis and save the human and time costs of analyzing large-scale data sets. However,the most important issue is how to set the extraction and classification criteria, and the performance of the created algorithmsand models depends on the quality of the training data prepared. Recently, fine-tuned a large-scale generic language modelcreated by unsupervised learning to adapt it to the task is often used in the field of natural language processing. The use ofgeneral-purpose language models also enables efficient execution of freestyle sentences clustering and visualization. I willexplain the design of annotation guidelines for freestyle sentences in training data creation, preprocessing, and task adaptationof general-purpose language models, introducing examples from previous studies. I will also briefly describe the deep learningtechnique Transformer networks, compare it with conventional probabilistic models (Hidden Markov Models, ConditionalRandom Fields, etc.) and discuss its advantages in freestyle sentence questionnaire analysis.


Learning a Bimodal Emotion Recognition System Based on Small Amount of Speech Data

2022 8th International Conference on Systems and Informatics (ICSAI)

This paper presents a bimodal emotion recognition system based on the voice and text information using small amount of speech data. Specifically, speech is divided into voice and text to learn emotion classifiers for each modal. The probabilities obtained from these emotion classifiers are weighted based on Mehrabians rule and summed up for each emotion to calculate the final score for bimodal emotion recognition. To create a highly accurate system while solving the problem that there are few Japanese speech data with emotion labels, we propose a novel data augmentation method and employ a transfer learning approach based on a pre-trained VGG16 model and a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model on the tweets. In order to prove the effectiveness of the proposed method, we revealed the recognition results for 7 emotional states, i.e., anger, sadness, joy, fear, surprise, disgust, and neutral. The experiment result suggested that our novel data augmentation method improved the accuracy and that bimodal predictions based on voice and text-based outperformed the single-model predictions.


Facial Expression Emotion Recognition Based on Transfer Learning and Generative Model

Facial expression emotion recognition has been a popular research topic, which played an important role in assisting the natural human-machine conversation. The conventional method for emotion estimation from facial expressions is to learn a CNN-based image classification model from scratch, However, learning such model requires a large number of labeled facial expression images, which is still a limited resource until now. To solve this problem, we propose a data augmentation method based on StyleGAN2 to generate artificial expression images with respect to seven emotions and use them as the additional training data. We further train an expression emotion recognition model based on a VGG16 network through transfer learning. In this research, we proposed a method using transfer learning and augmented images of facial expressions using trained VGG16 and StyleGAN2 and conducted experiments to achieve higher recognition accuracy for racial expression emotion recognition. Our experiment based on the CFEE dataset suggested that an emotion recognition accuracy of 75.10% could be obtained through transfer learning and the accuracy could further improved to 82.04% with the augmented expression images.


Difficulties in Nursing Practice Content and its Reasons for Patients with Acute Stroke


Inter-Rater Reliability Between the Results of Human Judgments of Emotions and those of Empathy


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.


Annotation and Evaluation of Utterance Intention Tag for Interview Dialogue Corpus

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

In this paper, we proposed the utterance intention tags for an interview dialogue corpus. We constructed an interview dialogue corpus with the tags we designed. Three or five annotators annotated the tags to the interview dialogue corpus (the total number of 49999 utterances) consisting of 30 dialogues. We conducted an evaluation experiment using Fleiss's kappa value to evaluate the reliability of the proposed tags. When three annotators annotated 18 different tags to the corpus, we obtained the kappa value of 0.55.


Topic Segmentation for Interview Dialogue System

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.


Development issues on communication robots' application in order to use for the older people care


Human operator's "Characteristic of autonomic nerve activity as psychological burden" and " Clarification of thinking process for AI development" while operating the humanoid robot conversation program


Examination to develop the artificial intelligence through transforming tacit knowledge of nurses' dialogue for patients with dementia towards explicit knowledge


Issues in developing natural language processing applications for healthcare robots to motivate older people while rehabilitation


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


Factor Affecting Low-Arch and High-Arch of Foot

13th International Society of Physical and Rehabilitation Medicine (ISPRM 2019)


Body Factors of Low Arch of Foot for Middle and Senior Age

World Confederation for Physical Therapy (WCPT2019)


Extended Dataset for Food Image Classification Based on Twitter

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


Data Augmentation for Lyric Emotion Estimation

2018 International Conference on Creativity, Innovation, and Invention in Digital Technology (CIIDT2018)

歌詞感情推定が実現すれば，テキスト検索やメロディ検索だけでなく，歌詞に含まれる感情や楽曲全体の感情の流れに基づいた楽曲検索システムおよび楽曲推薦システムが実現できる．そのためには，フレーズごとの歌詞感情コーパスが必要となる．しかし，フレーズの感情は人手で付与されるため，大規模な歌詞感情コーパスを作成するのは難しい．本稿では，歌詞感情コーパスの拡張手法を提案する．拡張前後の歌詞感情コーパスでそれぞれ学習した畳み込みニューラルネットワークを用い，提案手法の有効性を検証する．推定する歌詞の感情はJoy，Love，Anger，Sorrow，Anxietyの5クラスとする．


Lyric Emotion Estimation Using Word Embedding Learned from Lyric Corpus

Proceedings of the 4th IEEE International Conference on Computer and Communications

In searching huge music databases, users typically use title, artist or song writer's name as their search query. When users have only vague search conditions, searching for a song using affective information - that is, emotional impressions of the piece such as relief or sorrow - can be an appealing possibility. For cases in which the song's audio information consists of vocal and instrumental performances, both words and music reflect the writer's thoughts and emotions and encourage the imagination of the listeners. While a number of studies have used audio and lyric features in the emotion estimation of songs, many of the studies that use only lyrics have been based on existing textbook emotion estimation methods. Because the way in which words used in song lyrics tends to differ from the way they are used in text sentences, an emotion estimation method specific to song lyrics would be more appropriate. In this study, we extract features from a lyric corpus to handle lyric-specific expressions and propose a method to estimate emotion from each lyric phrase. The results of our evaluation experiment confirmed the effectiveness of the proposed method (lyric corpus word vector + 3-NN), as it produced a higher F-measure than the other methods.


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.


Exploring the Relations Between Posts and Comments for Short Text Conversation

Proceedings of The 12th International Conference on Natural Language Processing and Knowledge Engineering (NLP-KE17)

Chinese Association for Artificial Intelligence


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.


Professional Nurses Attitudes Towards the Introduction of Humanoid Nursing Robots (HNRs) to the Hospital

International Association for Human Caring 36th International Conference


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.


A Joint Prediction Model for Multiple Emotions Analysis in Sentences

14th International Conference on Intelligent Text Processing and Computational Linguistics

In this study, we propose a scheme for recognizing people's multiple emotions from Chinese sentence. Compared to the previous studies which focused on the single emotion analysis through texts, our work can better reflect people's inner thoughts by predicting all the possible emotions. We first predict the multiple emotions of words from a CRF model, which avoids the restrictions from traditional emotion lexicons with limited resources and restricted context information. Instead of voting emotions directly, we perform a probabilistic merge of the output words' multi emotion distributions to jointly predict the sentence emotions, under the assumption that the emotions from the contained words and a sentence are statistically consistent. As a comparison, we also employ the SVM and LGR classifiers to predict each entry of the multiple emotions through a problem transformation method. Finally, we combine the joint probabilities of the multiple emotions of sentence generated from the CRF-based merge model and the transformed LGR model, which is proved to be the best recognition for sentence multiple emotions in our experiment.


Recognition of Multiple Emotion States of Words using Multi-engine System

11th International Conference on Computer Applications 2013

In this paper, we propose a multi-engine voting system to recognize the multiple emotion states expressed in the Chinese words according to the word identity and the context information, which can better reflect the people's real emotion states compared to previous works focused on single emotion analysis. Conditional Random Field and Maximum Entropy are employed to learn two probabilistic models to predict the possible emotions and assign the corresponding probability scores to the eight basic emotion types as well as the no-emotion type. To solve the problem of imbalanced data source, we introduce a threshold to improve the recognition of no-emotion and other word emotions. Our experiment achieves promising results which prove that multiple emotion states can be better recognized by the multi-engine system rather than either Conditional Random Field or Maximum Entropy


Emotion Estimation from Sentence Using Relation between Japanese Slangs and Emotion Expressions

Proceedings of 26th Pacific Asia Conference on Language,Information and Computation

Most of Japanese slang words such as Wakamono Kotoba are analyzed as unknown word or segmented wrongly by the morphological analysis system. These problems are causing negative effect on sentiment analysis in text. These words generally have many varieties of notations and conjugations, and they lack versatility. As a result, many of them are not registered in the dictionaries, making morphological analysis more difficult. In this paper, we aimed to decrease such negative effects of Wakamono Kotoba for the accuracy of emotion estimation from sentence and proposed a method to increase the accuracy by using a classification method based on machine learning. In this method we used emotional expressions which had high relevance with Wakamono Kotoba as feature. As a result, the proposed method obtained 20% higher accuracy than the method only using morpheme Ngram as feature.


Judgment of Depressive Tendency from Emotional Fluctuation in Weblog

Proceedings of the 16th International Conference on Knowledge-Based and Intelligent Information & Engineering Systems - KES2012

Some people who have tendency toward depression describe their inner thoughts vividly in their weblogs. Such descriptions are sometimes made consciously but at other times unconsciously by the authors. If such unconscious authors could become aware of their own symptom of the depression, that would contribute to early detection and treatment of the sickness. To achieve this, it is essential to find out the tendency of depression from the descriptions in weblog. Because the symptom of depression is not static, we attempted to detect the tendency of depression focusing on the temporal fluctuation of emotion of the authors. As many weblogs describe what happened or what the authors felt in daily life like a personal journal, we thought that weblogs were ideal for obtaining the time-varying emotion of the authors. In this paper, we proposed the method to detect the tendency of depression in weblog. Our method is based on the temporal fluctuation of emotion described by the authors.


Exploring Emotional Words for Chinese Document Chief Emotion Analysis

Proceedings of 25th Pacific Asia Conference on Language, Information and Computation(PACLIC2011)

In this study, we develop a document emotion analysis model by making use of the function of emotional words annotation. Eight basic emotion categories have been selected, including Expect, Joy, Love, Surprise, Anxiety, Sorrow, Anger and Hate, for both the word and the document level emotion analysis. We introduce two parameters, term relevance and term frequency, to evaluate the relations between the word emotions and the document emotions. Promising experimental results reveal the effectiveness of our document emotion analysis model under different emotion situations.


The 25th Pacific Asia Conference on Language, Information, and Computation (PACLIC25)


Emotion Estimation of Wakamono Kotoba Based on Distance of Word Emotional Vector

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

Recently, there are many research that aim at estimating emotion from text. The meaning of language expression that we use in daily life varies depending on the context used. In that sense, language expression conveys ambiguous information. Wakamono Kotoba are words used by young people, and they also have semantic ambiguity. Most of the Wakamono Kotoba are not registered in the existing dictionaries, making it difficult for a system to recognize the meaning of Wakamono Kotoba. In this paper, we propose a method for estimating emotion in Wakamono Kotoba that were not registered in the system, by using Wakamono Kotoba example sentences as features. The proposed method applied Earth Movers Distance to vector of words. As a result of the evaluation experiment, higher estimation accuracy was obtained by considering emotional distance between words -- an approach that had not been used in the conventional research -- than by using only word importance value.


Modification Relations based Emotional Keywords Annotation using Conditional Random Fields

The 4th International Conference on Intelligent Networks and Intelligent Systems (ICINIS2011)


Modification Relations Based Emotional Keywords Annotation Using Conditional Random Fields

Proceedings of 2011 Fourth International Conference on Intelligent Networks and Intelligent Systems

In this study, we demonstrate the effectiveness of modification features for emotional keywords annotation on Chinese sentences extracted from a Chinese emotion corpus. Eight basic word emotion categories have been selected and Conditional Random Fields is employed in eight experiments with different sets of features. Our result shows that modification features make the contribution to emotional keywords annotation.


Construction of Wakamono Kotoba Emotion Dictionary and Its Application

Computational Linguistics and Intelligent Text Processing

Currently, we can find a lot of weblogs written by young people. In the weblogs, they tend to describe their undiluted emotions or opinions. To analyze the emotions of young people and what causes those emotions, our study focuses on the specific Japanese language used among young people, which is called Wakamono Kotoba. The proposed method classifies Wakamono Kotoba into emotion categories based on superficial information and the descriptive texts of the words. Specifically,the method uses literal information used for Wakamono Kotoba, such as Katakana, Hiragana, and Kanji, etc., stroke count, and the difficulty level of the Kanji as features. Then we classified Wakamono Kotoba into emotion categories according to the superficial similarity between the word and the Wakamono Kotoba registered in the dictionary with an annotation of its emotional strength level. We also proposed another method to classify Wakamono Kotoba into emotion categories by using the cooccurrence relation between the words included in the descriptive text of the Wakamono Kotoba and the emotion words included in the existing emotion word dictionary. We constructed the Wakamono Kotoba emotion dictionary based on these two methods. Finally, the applications of the Wakamono Kotoba emotion dictionary are discussed.


Needs and challenges of care robots in nursing care setting: A literature review

This study aims to identify needs and challenges of care robot in nursing care setting through an extensive search of the literature. As the result shows, there exists a shortage of information about results of the introduction of care robots, the needs of recipients and care providers, and relevant ethical problems. To advance our research and to introduce care robots into setting, there are so many things to do; consider the application of natural language processing technology by collaborating with researchers in the robotics field, carry out an investigation, extract the needs, clarify ethical problems and seek solutions, conduct the on-site experiment study, and so on.


The present conditions, problems and future direction of the server-controlled clinical pathway system development in psychiatric hospitals

Clinical pathways used today are paper-based, although many different kinds of clinical pathway are used in clinical practice. However, in the development of software for clinical pathway, it is difficult to achieve cooperation between medical experts, who are not used to expressing their ideas and work in words, and system-developers whose medical knowledge is limited. As a consequence, the current situation is that medical practitioners make their own software and use it in their practice. While this may work, it is less than ideal because refinements that may make the software most effective through engineering expertise is not used. Thus, in our research team, the nurse researchers and the engineering researchers cooperated and developed a clinical pathway system. In this paper, the present conditions, problems, and future direction of the server-controlled CP system development in the psychiatric hospitals, is discussed from viewpoint of nursing as a user.


Flexible English Writing Support Based on Negative-Positive Conversion Method

With development of the recent globalization, thechance to exchange in English increased in the business field.In particular, its necessary to write a thesis and a charterhandwriting in English. Because many Japanese are notused to making English sentence, it is a great burden towrite appropriate sentence in English without any supportfor creating English sentence. In this study we havedeveloped an English composition support system. By thissystem, its to search for the interlinear translation exampleto refer to by database and generate a new sentence byreplacing a noun in the example sentence. In this paper,based on the technique of Super-Function, we propose amethod to convert an affirmative sentence into negativesentence and vice versa to realize more flexible andextensive text conversion.


Wakamono Kotoba Emotion Corpus and Its Application for Emotion Estimation

Proceedings of 2010 International Conference on Advanced Intelligence

In this paper, we describe how we constructed the linguistic resources of Wakamono Kotoba emotion corpus to be used for emotion recognition and explain the features we obtained from statistical analysis. Our Wakamono Kotoba emotion corpus includes Japanese language used by young people and language for expressing emotion. These were mainly gathered from Weblogs that were written by young people from their teens to their twenties.


Computerized electoronic nursing staffa daily records system in the Apsychiatric hospital: present situation and future prospects


Construction of English Composition Support System Based on Conveying Emotion

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

With the increased demand for English communication, various styles of learning support methods have been proposed and provided to the Japanese learners. However, there are still many learners finding it hard to read, write and speak in English. Regardless of language difference, understanding the other's intention and emotional status accurately and expressing what they think or feel to the others appropriately are the important abilities for actual communication. This paper focuses on the linguistic performance ability especially to communicate emotion and introduce our prototype system that help describe the emotion in English.


Estimate Word Emotions Based on Part of Speech and Positional Information

The 5th International Conference on Intelligent Computing


An Approach for Evaluating Emotion Tagged Corpus

IADIS International Conference Interfaces and Human Computer Interaction 2008

This paper proposes a method for evaluating our emotion corpus and describes the result of evaluation. The emotion corpus was originally created in Japanese and English to be used for emotion recognition research and consisted of dialogue sentences including emotional expressions. To ensure the reliability and use the corpus as the standard of emotion evaluation, the objective evaluation is necessary because single person annotated tags on the corpus. For Japanese part of corpus evaluation, we conducted a questionnaire to the several examinees. The examinees were asked to tag arbitrarily emotion categories on the sentences in the corpus. The obtained sentence emotion tags were classified then the match rate of the initial tag and the tags annotated by the examinees. Reliability of each emotion tag annotated on the sentence was calculated and added as emotional weight on each sentence emotion tag. The 10-fold cross validation test was conducted using the weight added corpus to estimate sentence emotion and obtained recall of 0.62 on average.


An Algorithm for Extracting Emotion Occurrence Sentence Pattern from Corpus

In this paper, we propose a method of extracting sentence patterns for estimating speaker's emotion.Our method estimates speaker's emotion by using a dictionary that registered the sentence pattern extracted from sentences expressing speaker's emotion.Because sentence types expressing emotion are various, we focused on dialogue sentence that includes important elements such as subject or object and proposed a method to extract the sentence pattern automatically.This paper describes the accuracy for converting the sentence including emotional expressions into the sentence patterns.We conduct experiments for emotion estimation based on the sentence pattern then describe the result of the evaluation.


Comparison of corpus-based emotion estimation methods

Proceeding of the 10th IASTED International Conference Intelligent Systems and Control

Internationalization of information is increasing needs for a computer to deal with multilingual communication. Understanding not only semantic information but also underlying emotion in textual information can contribute to assist such communication and also to make human-computer interaction more natural and congenial. For the purpose of emotion estimation from multilingual text, we created bilingual emotion annotated corpus in Japanese and English. Based on the corpus, we statistically analyzed the characteristics of how word emotion affects sentence emotion in each language. Two kinds of emotion estimation methods were proposed by using the existing techniques considering the feature quantity obtained from the corpus statistics then evaluation experiments were carried out.


Outcome Management and Morphologic Variance Analysis Using PsychomsTM for Patient Care in Psychiatric Hospitals

In 2003, the Japanese government set an approximate goal of 72,000 social hospitalization in-patients being discharged in 10 years from psychiatric hospitals to the community. To meet this challenge, the clinical pathway (CP) is a useful model for managing the team's progress in providing health care services. High quality discharge planning for patients requires continuous refinement of CP including the collection and analysis of variance data, i.e., information not listed in the CP model. The purpose of this study is to analyze variance data gathered using PsychomsTM to find unidentified issues with keyword sorting (keyword variance analyses system) and text-mining (text mining variance analyses system). Using morphological analysis to detect word occurrences in variance data, it may be possible to identify problems needing solution by developing automatic rankings to analysis outcomes. To achieve this, however, specialized experience is required for variance analysis. Also, there must be high congruence between the results of text mining variance analyses and keyword variance analyses systems.


Corpus Based Analysis of Japanese-English Emotional Expressions

Development of information technology has increased the chances of interaction between human and computer. Understanding not only semantic information but also underlying emotion will make communication with computer more natural and congenial. As preliminary research for emotion estimation from multilingual text, we focused on emotion expressions of Japanese and English and created a corpus with about 1,200 bilingual sentences and tagged emotions on words and sentences. Based on the corpus, we statistically analyzed the characteristics of how emotions were expressed in each language and also the relationships between sentence emotion and word emotion in the sentence. The results show the characteristics of emotion expressions in Japanese and English dialogue sentences.


Electroencephalograph Estimation Method of Measuring `Empathic Understanding'

It is important for health care staff to understand patients' emotion and affective changes. Health care staffs should understand the patients' world as if this was his her own. This is called "empathic understanding". To understand the emotional process, electroencephalograph recording (EEG) was performed on five healthy participants while watching the sentimental movie which was 24-minutes long. The EEG changes during the emotional scenes were collected and compared with emotionally stable states. By using power map and spectrogram analyses of EEG, alpha bands at the occipital region were seen specifically among the emotionally impressed states. It seemed that the changes indicated the objective expression of empathic understanding. We suggest that EEG with power map and spectrogram analyses is a useful fundamental tool for developing a second generation computer system adjusted to the changes of one's mind. However, further examinations to remove artifacts among the EEG results are needed.


An Emotion Estimation Method for Emotion Interface

Proceedings of International Symposium on Artificial Intelligence and Affective Computing 2006

The existing approaches of emotion estimation from a textwere mainly focused on estimating positive or negative emotions in thetext. However human emotions are more complex and we cannot judgeour emotions only by positive or negative. In this paper, we proposedan emotion estimation algorithm for conversation sentence. We gave therules of emotion occurrence to 1616 sentence patterns. In addition, we developeda dictionary which consisted of emotional words and emotionalidioms. The proposed method can estimate emotions in a sentence bymatching the sentence pattern of emotion occurrence and the rule. Furthermore,we can get two or more emotions included in the sentence bycalculating emotion parameter. We constructed the experiment systembased on the proposed method for evaluation. We analyzed weblog dataincluding 1645 sentences by the system, and conducted the experimentto evaluate emotion estimation accuracy.


Psychoms: A mental health patient management and variance analysis system using artificial intelligence

Proceedings of The Fourth International Conference on Information and The Fourth Irish Conference on the Mathematical Foundations of Computer Science and Information Technology

Although it is agreed that there is a need for clinical pathway variance analysis, methods for creating a system are less well defined. The purpose of this paper is considering a way to develop the Psychoms that mental health patient management and variance analysis system using artificial intelligence. This system aims to improve a patient's quality of life through consistent and timely care and has potential to assist in the improvement of quality health care services. This article describes a method of outcomes management and variance analysis in this system and the prospective development of future research are discussed.


Consideration of Measuring Method for Empathic Understanding as Technological Competency of Nursing


Research on development of the Hospital Management Outcomes Analysis System for using in psychiatric hospitals

Proceedings of the Ninth IASTED International Conference on Artificial Intelligence and Soft Computing

Recently, clinical pathways (CP) are often implemented to improve care, yet their effect on quality of care and outcomes have not been generally evaluated. Especially, the item of CP and the outcome management for the patient with mental disorders is not examined enough in Japan. The purpose of this paper is to examine the development of prototype Hospital Outcomes Analysis System (HOAS). As the first step of HOAS development, authors considered to create CP and outcome management item for people suffering schizophrenia for the improvement of quality mental health care. Comprehensive survey interviews were conducted for the hospital staff. CP for the patient with schizophrenic and the critical criteria for patient outcome management (OMCP) were developed from these surveys. Additionally, in order to fully utilize CP, it is very important to develop the efficient and effective means for managing patient outcomes. The CP and outcome management in the hospital may bring patients and their families in progress QOL, in addition to job satisfaction of care-givers. As the second step, we developed a prototype HOAS based on OMCP during the whole period from hospitalization to discharge of which patient outcomes management is done.


Estimating Human Emotions using Words and Sentence Patterns

IEEE International Conference on Information Acquisition

Our research group proposes a method to recognize and create human emotions which can be applied to a robot used for welfare services. We also aim to develop an affective computer with enough ability to communicate that prople don't sense a disconnect. In this paper, an "emotion estimation module" based on words for recognizing human emotions. This mudule extracts "emotion occurrence condition" based on emotions contained in words and sentence meanings, and estimates the emotion of a speaker or writer. The meterials used for the module are collected from written quotations from conversations and internet blogs. We constructed a prototype system based on the proposed method and conducted an experiment on sentences taken from internet blogs in order to verify the effectivenss of the method.


An Algorithm for Measuring Human Emotions based on Context and Sentence Pattern

Proceedings of the third International Conference on Information

It is necessary to nurse the senior citizen for declining population of children and increasing nuclear family. We are researching and developing a welfare robot which has a sensibility to support the life of the senior citizen and to reduce nursing. One of the basic researches on such welfare robots is to measure the sensibility from conversation sentences. In this paper, an algorithm for measuring the sensibility is presented, and a prototype system based on the proposed algorithm has been constructed.


Anonymization of indirect personal information in dialog text

対話テキストにおける間接識別情報の匿名化

電気学会全国大会講演論文集

The Institute of Electrical Engineers of Japan

近年様々な分野で，テキストデータの活用が進められており，個人情報の匿名化に対する需要が高まっている．個人情報には，それ単体で個人を識別可能な直接識別情報と複数組み合わせることで個人を識別可能な間接識別情報がある．既存のテキストデータに対する匿名化研究において，これらの違いに着目した研究は少ない ．また，主な匿名化方法として，個人をk人以下に特定されないようにするk匿名化があるが，これは医療用データベースのように多人数の個人情報が含まれること を前提としており，1対1の対話テキストなどの個人情報が含まれている個人が少人数のデータに対しては有効でない．そこで本研究では，1対1の対話テキストに対 して，間接識別情報を適切に匿名化することで，匿名化による情報量の減少を抑えた匿名加工情報の作成を目的とする．


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

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．


感情分析のためのカウンセリングマルチモーダルデータセットの構築および評価

感情分析のためのカウンセリングマルチモーダルデータセットの構築および評価

情報処理学会全国大会講演論文集

情報処理学会全国大会講演論文集

コロナ禍をきっかけとして，オンラインカウンセリングが急激に普及した．オンラインカウンセリングは，細かな表情や声色の変化の認識が難しいことが問題点として挙げられる．カウンセラーの判断を助け，業務効率化のために，クライアントの感情の自動分析が有効である．人間は相手の感情を読み取るとき，声色，表情，発話内容など複数の要素から総合的に判断を行っている．AIによる感情推定も同様に，複数の異なるモダリティを組み合わせて用いるマルチモーダル感情推定により，高精度な感情分析が可能になると考えられる．マルチモーダル感情推定には，学習用データとして，感情のラベルが付与されたマルチモーダルデータセットが必要となる．本研究では，オンラインカウンセリング中のクライアントの感情分析のためのマルチモーダルデータセットを構築し，その評価を行う．本データセットには，カウンセラーによる労務者を対象としたオンラインカウンセリングにおける労務者の様子を収録した動画と，第三者により付与された客観感情ラベルが含まれている．


Estimating Utterance Intentions in Interview dialogue

インタビュー対話における発話意図の推定

電気学会全国大会講演論文集

対話システムがユーザと自然なやり取りをするためには，ユーザの発話意図を正しく理解する必要がある．発話意図を推定する手法として，発話文に対して二段階で分析をして発話意図を推定する手法 (1)などがある．これらの手法は修飾節等を含む発話文全文に対して適用される．発話意図は修飾節等がなくても推定できると考えられる．本稿では，発話文を構文解析し，述語動詞とそれにかかっている部分のみに対して発話意図を推定する手法を提案する．また，本研究では大規模言語モデルT5 (Text-to-Text Transfer Transformer)を使う．


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．


情報リテラシー科目におけるAI構築課題の取り組み事例

情報リテラシー科目におけるAI構築課題の取り組み事例

第29回FDフォーラム 第3分科会 大学教育におけるAIの活用ならびにその弊害

第29回FDフォーラム 第3分科会 大学教育におけるAIの活用ならびにその弊害

第29回FDフォーラム 第3分科会 大学教育におけるAIの活用ならびにその弊害

第29回FDフォーラム 第3分科会 大学教育におけるAIの活用ならびにその弊害


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.