Microblog sentiment analysis based on emoticon networks model

Authors:
Lumin Zhang;Shaojie Pei;Lei Deng;Yi Han;Jinhui Zhao;Feng Hong
Affiliations:
National University of Defense Technology, Changsha, China;National University of Defense Technology, Changsha, China;National University of Defense Technology, Changsha, China;National University of Defense Technology, Changsha, China;National University of Defense Technology, Changsha, China;Xi'an Satellite Control Center, Xi'an, China
Venue:
Proceedings of the Fifth International Conference on Internet Multimedia Computing and Service
Year:
2013

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Abstract

With the repaid development of Internet and communication technologies, microblog has become a valuable social media for public sentiment analysis. Emoticons, strongly associated with subjectivity and sentiments, are also increasing popular for users to directly express their feelings, emotions and moods in microblog platforms. In this paper, we address the problem of public sentiment analysis by leveraging emoticons, and develop emoticon networks approaches. Based on large-scale corpus, we use FP-growth algorithm combining with retrieve distance to aggregate similar emoticons, and build emoticon networks model based on Mutual Information. Then, we propose a microblog orientation analysis framework for both emoticon messages and non-emoticon messages. Experimental evaluations show that our approach could perform effectively for microblog sentiment analysis. Although we worked with Chinese in our research, the technique can be used with any other language.