Every Term Has Sentiment: Learning from Emoticon Evidences for Chinese Microblog Sentiment Analysis

Chinese microblog is a popular Internet social medium where users express their sentiments and opinions.But sentiment analysis onChinese microblogs is difficult: The lack of labeling on the sentiment polarities restricts many supervised algorithms; out-of-vocabulary words and emoticons enlarge the sentiment expressions, which are beyond traditional sentiment lexicons. In this paper, emoticons in Chinese microblog messages are used as annotations to automatically label noisy corpora and construct sentiment lexicons. Features including microblog-specific and sentimentrelated ones are introduced for sentiment classification. These sentiment signals are useful for Chinese microblog sentiment analysis. Evaluations on a balanced dataset are conducted, showing an accuracy of 63.9% in a threeclass sentiment classification of positive, negative andneutral.The features mined from the Chinese microblogs also increase the performances.