ZTE Communications ›› 2017, Vol. 15 ›› Issue (S2): 30-37.DOI: 10.3969/j.issn.1673-5188.2017.S2.005

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Emotion Analysis on Social Big Data

REN Fuji, Kazuyuki Matsumoto   

  1. Tokushima University, Tokushima7708506, Japan
  • Received:2017-08-11 Online:2017-12-25 Published:2020-04-16
  • About author:REN Fuji (ren@is.tokushima-u.ac.jp) received his B.E. and M.E. degrees from Beijing University of Posts and Telecommunications, Beijing, China in 1982 and 1985, respectively. He received his Ph.D. degree in 1991 from Hokkaido University, Japan. From 1991, he worked at CSK, Japan, where he was a chief researcher of NLP. From 1994 to 2000, he was an associate professor in the Faculty of Information Sciences, Hiroshima City University, Japan. He became a professor in the Faculty of Engineering of the University of Tokushima, Japan in 2001. His research interests include natural language processing, artificial intelligence, language understanding and communication, and affective computing. He is a member of IEICE, CAAI, IEEJ, IPSJ, JSAI, and AAMT, and a senior member of IEEE. He is a fellow of the Japan Federation of Engineering Societies. He is the president of the International Advanced Information Institute.|Kazuyuki Matsumoto (matumoto@is.tokushima-u.ac.jp) received the Ph.D. degree in 2008 from Tokushima University, Japan. He is currently an assistant professor of Tokushima University. His research interests include affective computing, emotion recognition, artificial intelligence, and natural language processing. He is a member of IPSJ, ANLP, JSAI, IEICE and IEEJ.


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 high-quality emotion corpus and the automatic-constructed corpus that we created in our past studies, and then analyze a large-scale corpus consisting of Twitter tweets based on the model. As the result of time-series analysis on the large-scale corpus and the result of model evaluation, we show the effectiveness of our proposed method.

Key words: emotion analysis, social big data analysis, affective computing