Chinese microblog is a popular Internet social medium where users express their sentiments and opinions. But sentiment analysis on Chi- nese microblogs is difficult: The lack of labeling on the sentiment polarities restricts many supervised algorithms; out-of-vocabulary words and emoti- cons enlarge the sentiment expressions, which are beyond traditional sen- timent 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 sentiment- related 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 three- class sentiment classification of positive, negative and neutral. The features mined from the Chinese microblogs also increase the performances.
['Fei Jiang', 'Anqi Cui', 'Yiqun Liu', 'Min Zhang', 'Shaoping Ma']
Every Term Has Sentiment: Learning from Emoticon Evidences for Chinese Microblog Sentiment Analysis