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Predicting Complex Word Emotions and Topics through a Hierarchical Bayesian Network

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China Communications 2012年第3期9卷 99-109页

作者： Kang Xin REN Fuji Univ Tokushima Fac Engn Tokushima 7708501 Japan Hefei Univ Technol AIM Hefei 230009 Peoples R China

In this paper,we provide a Word Emotion Topic(WET) model to predict the complex word emotion information from text,and discover the distribution of emotions among different topics.A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories:joy,love,expectation,surprise,anxiety,sorrow,anger and hate.We use a hierarchical Bayesian network to model the emotions and topics in the text.Both the complex emotions and topics are drawn from raw texts,without considering any complicated language features.Our experiment shows promising results of word emotion prediction,which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields(CRFs) on raw text.We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.

关键词：贝叶斯网络模型网络预测情感隐马尔可夫模型复合电子运动模型预测 Word

Exploring Latent Semantic Information for Textual Emotion Recognition in Blog Articles

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IEEE/CAA Journal of Automatica Sinica 2018年第1期5卷 204-216页

作者： Xin Kang fuji ren Yunong Wu Faculty of Engineering Tokushima University 2-1 Minamijyousanjima-cho Tokushima 770-8506 Japan IEEE

Understanding people's emotions through natural language is a challenging task for intelligent systems based on Internet of Things(Io T). The major difficulty is caused by the lack of basic knowledge in emotion expressions with respect to a variety of real world contexts. In this paper, we propose a Bayesian inference method to explore the latent semantic dimensions as contextual information in natural language and to learn the knowledge of emotion expressions based on these semantic dimensions. Our method synchronously infers the latent semantic dimensions as topics in words and predicts the emotion labels in both word-level and document-level texts. The Bayesian inference results enable us to visualize the connection between words and emotions with respect to different semantic dimensions. And by further incorporating a corpus-level hierarchy in the document emotion distribution assumption, we could balance the document emotion recognition results and achieve even better word and document emotion predictions. Our experiment of the wordlevel and the document-level emotion predictions, based on a well-developed Chinese emotion corpus ren-CECps, renders both higher accuracy and better robustness in the word-level and the document-level emotion predictions compared to the state-of-theart emotion prediction algorithms.

关键词： Bayesian inference emotion-topic model emotion recognition multi-label classification natural language understanding

Semi-Automatic Complex Emotion Categorization and Ontology Construction from Chinese Knowledge

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China Communications 2012年第3期9卷 28-37页

作者： Jiang Peilin Wang Fei REN Fuji Xi An Jiao Tong Univ Inst Artificial Intelligence & Robot Xian 710049 Peoples R China Univ Tokushima Grad Sch Adv Technol & Sci Tokushima 7708506 Japan Hefei Univ Technol AIM Hefei 230009 Peoples R China

In order to recognize one's intention from the communication,both the meaning and the emotion are necessary to be interpreted correctly.But until now the study of fine-grained theory of emotion is still full of challenges.This paper analyzes emotion category according to the statistics of Affective Word(AW) hierarchy and describes an emotion ontology from Chinese knowledge resource semi-automatically created for human machine interaction.The emotion hierarchy is called complex emotion.Firstly,over 7 000 AWs have been annotated and their detailed explanations had been collected for an affective lexicon and then the consistent relationships are automatically parsed and a serial of emotion hierarchical structures are built up.More than 50 affective categories are extracted by a lexical clustering algorithm and about 5 000 nouns and adjectives and 2 000 verbs are categorized into the predicate hierarchy.The results have been evaluated to be valid by two metrics.

关键词：知识资源层次分类本体论中国半自动电子运动自动气象站相互作用

Hierarchical Network with Label Embedding for Contextual Emotion Recognition

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Research 2021年第1期2021卷 103-111页

作者： Jiawen Deng fuji ren Faculty of Engineering Tokushima UniversityTokushima 770-8506Japan

Emotion recognition has been used widely in various applications such as mental health monitoring and emotional management.Usually,emotion recognition is regarded as a text classification task.Emotion recognition is a more complex problem,and the relations of emotions expressed in a text are nonnegligible.In this paper,a hierarchical model with label embedding is proposed for contextual emotion recognition.Especially,a hierarchical model is utilized to learn the emotional representation of a given sentence based on its contextual information.To give emotion correlation-based recognition,a label embedding matrix is trained by joint learning,which contributes to the final prediction.Comparison experiments are conducted on Chinese emotional corpus renCECps,and the experimental results indicate that our approach has a satisfying performance in textual emotion recognition task.

关键词： emotional contextual has

Message from the Guest Editor for the Special Issue

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China Communications 2012年第3期9卷 I0002-I0003页

作者： REN Fuji the Faculty of Engineering of the University of Tokushima Japan the International Advanced Information Institute Chinese Association of Artificial Intelligence

Special Issue on Using Artificial Intelligence in Internet of Things Guest Editors: fuji ren, Yu Gu

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ZTE Communications 2014年第4期12卷 2-2页

作者： fuji ren Yu Gu Univ. of Tokushima Hefei University of Technology

Interact of Things has received much attention over the past de cade. With the rapid increase in the use of smart devices, we are now able to collect big data on a daily basis. The data we are gather ing and related problems arc becoming more complex and uncer tain. Researchers have therefore turned to AI as an efficient way of dealing with the problems created by big data.

关键词： AI ZTE Communications Call for Papers Special Issue on Using Artificial Intelligence in Internet of Things

DFF-ResNet: An Insect Pest Recognition Model Based on Residual Networks

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Big Data Mining and Analytics 2020年第4期3卷 300-310页

作者： Wenjie Liu Guoqing Wu fuji ren Xin Kang the School of Information Science and Technology and School of Transportation and Civil Engineering Nantong UniversityNantong 226019China the Faculty of Engineering Tokushima UniversityTokushima 770-8506Japan the School of Information Science and Technology Nantong UniversityNantong 226019China

Insect pest control is considered as a significant factor in the yield of commercial crops.Thus,to avoid economic losses,we need a valid method for insect pest recognition.In this paper,we proposed a feature fusion residual block to perform the insect pest recognition task.Based on the original residual block,we fused the feature from a previous layer between two 11 convolution layers in a residual signal branch to improve the capacity of the block.Furthermore,we explored the contribution of each residual group to the model performance.We found that adding the residual blocks of earlier residual groups promotes the model performance significantly,which improves the capacity of generalization of the model.By stacking the feature fusion residual block,we constructed the Deep Feature Fusion Residual Network(DFF-ResNet).To prove the validity and adaptivity of our approach,we constructed it with two common residual networks(Pre-ResNet and Wide Residual Network(WRN))and validated these models on the Canadian Institute For Advanced Research(CIFAR)and Street View House Number(SVHN)benchmark datasets.The experimental results indicate that our models have a lower test error than those of baseline models.Then,we applied our models to recognize insect pests and obtained validity on the IP102 benchmark dataset.The experimental results show that our models outperform the original ResNet and other state-of-the-art methods.

关键词： insect pest recognition deep feature fusion residual network image classification

Emotion Analysis on Social Big Data

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ZTE Communications 2017年第B12期15卷 30-37页

作者： REN Fuji Kazuyuki Matsumoto Tokushima University Tokushima7708506Japan

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

关键词： emotion analysis social big data analysis affective computin

Current Situation and Development of Intelligence Robots

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ZTE Communications 2016年第B12期14卷 25-34页

作者： REN Fuji SUN Xiao Anhui Key Lab of Affective Computing and Advanced Intelligence Machine Hefei University of Technology Hefei 230009 China Tokushima University Tokushima 770800 Japan

Industrial intelligent robots are treated as a measure of na- tional scientific level and technology innovation, and also the important symbol of high-level manufacturing, while service intelligent robots can directly affect people＇ s daily lives. The development of artificial robots in different areas is at- tracting much attention around the world. This article re- views the current situation and development of Chinese and international intelligent robot markets including industrial ro- bots and service robots. The intelligent robot technology and the classification of robots are also discussed. Finally, appli- cations of intelligent robots in various fields are concluded and the development trends and outlook of intelligent robots are explored.

关键词： intelligent robot artificial intelligence development trends