A web sentiment analysis method on fuzzy clustering for mobile social media users

Abstract

Micro-blog has become an emerging application in the Internet in recent years, and affective computing and sentiment analysis for micro-blog have been a vital research project in computer science, natural linguistics, psychology of human, and other social computing fields. In this paper, firstly, fuzzy clustering theory was introduced and source database for micro-blog was constructed. Then, word similarity computation method based on basic emotion word set of HowNet was used to calculate weights of micro-blog emotion words, and micro-blog emotional lexicon was built. Next, using calculation methods for appropriate sentiment value, the whole micro-blog message’s emotional values were obtained. Finally, sentiment values from users in different time periods were selected as original data matrix, using the fuzzy clustering algorithm. The users were classified dynamically; meanwhile, dynamic clustering figure was generated. The best classification was obtained by using F statistics test method, and emotion trend graph was predicted from classification results, to more intuitively analyze emotion changes of user. In this paper, using micro-blog information with affective computing, governments, businesses, or enterprises can get different classification results according to the different needs and take the appropriate measures.