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Indexed by:会议论文

Date of Publication:2018-01-01

Included Journals:CPCI-S

Volume:11168

Page Number:158-169

Key Words:Sentiment word; Relative branch entropy; Sentiment analysis

Abstract:There are a lot of new sentiment words appear in Weibo platform every day in the web2.0. As the unpredictable polarity of massive new words are detrimental to sentiment analysis for Weibo, hence how to extract new sentiment words and expand sentiment lexicon is of great importance. Therefore, we propose a relative branch entropy based method, which combines word frequency and adjacent words information to extract new sentiment word in Weibo. After integrated context and other factors, this method improves the accuracy of new sentiment word recognition. Three experiments are implemented on COAE 2014 Weibo corpus to compare the performance of different statistics with the proposed method. Experiment results show that the proposed method has a high accuracy, which demonstrates the effectiveness of this method and verify the promoted effect of new sentiment word extraction on the performance of Weibo sentiment classification.