Chinese Emotion Recognition Based on Three-Way Decisions
In recent years, affective computing has become a research hotspot in the area of natural language processing and Chinese emotion recognition is an important constituent. This paper proposes a method of Chinese emotion recognition based on three-way decisions. Given the emotion dictionary constructed firstly, the grammatical information of sentences, topic features of texts and three-way decisions are integrated and applied into Chinese emotion recognition, thus realizing the multi-label emotion recognition of sentences in Chinese texts. The results of experiments show that the method of Chinese emotion recognition, based on three-way decisions, has achieved excellent results in the emotion recognition of Chinese sentences.
KeywordsThree-way decisions Probability topic Emotion dictionary Affective computing
- 1.Hu, M., Liu, B: Mining and summarizing customer reviews. In: Proceedings of the 10th International Conference on Knowledge Discovery and Data Mining, pp. 168–177 (2004)Google Scholar
- 2.Liu, B., Zhang, L.: A Survey on Opinion Mining and Sentiment Analysis. Mining Text Data. Springer, New York (2012)Google Scholar
- 3.Jo, Y., Oh, A.H.: Aspect and sentiment unification model for online review analysis. In: Proceedings of the 4th ACM International Conference on Web Search and Data Mining, pp. 815–824 (2011)Google Scholar
- 5.Picard, R.W.: Affective Computing. MIT Press, Cambridge (1997)Google Scholar
- 7.Rao, D., Ravichandran, D.: Semi-supervised polity lexicon induction. In: Proceedings of the EACL, pp. 675–682 (2009)Google Scholar
- 8.Dave, K., Lawrence, S., Pennock, D.M.: Mining the peanut gallery: opinion extraction and semantic classification of product reviews. In: Proceedings of WWW-03, 12th International Conference on the World Wide Web, pp. 519–528. ACM, Budapest (2003)Google Scholar
- 11.Ren, F.: Document for Ren-CECps 1.0 (2009). http://a1-www.is.tokushima-u.ac.jp/member/ren/Ren-CECps1.0/Ren-CECps1.0.html
- 14.Tsoumakas, G., Katakis, I., Vlahavas, I.: Mining multi-label Data, Data Mining and Knowledge Discovery Handbook, 2nd edn. Springer, Heidelberg (2010). Part 6Google Scholar
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