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

摘要：

　　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.

关键词： Microblog Sentiment Analysis Sentiment Lexicon Construction Support Vector Machine

作者: Fei Jiang Anqi Cui Yiqun Liu Min Zhang Shaoping Ma

作者单位: State Key Laboratory of Intelligent Technology and Systems, Tsinghua National Laboratory for Information Science and Technology, Department of Computer Science and Technology, Tsinghua University, Beijing 100084, China

会议类型: 国际会议

会议名称: Second CCF Conference,NLPCC2013(第二届自然语言处理与中文计算会议)

会议地点: 重庆

会议语种:英文

页码: 224-235

在线出版日期: 2013-11-15（万方平台首次上网日期，不代表论文的发表时间）