Abstract
Chinese microblog is a popular Internet social medium where users express their sentiments and opinions. But sentiment analysis on Chinese 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 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.
This work was supported by Natural Science Foundation (61073071), National High Technology Research and Development (863) Program (2011AA01A207). Part of this work has been done at the NUSTsinghua EXtreme search centre (NExT).
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Jiang, F., Cui, A., Liu, Y., Zhang, M., Ma, S. (2013). Every Term Has Sentiment: Learning from Emoticon Evidences for Chinese Microblog Sentiment Analysis. In: Zhou, G., Li, J., Zhao, D., Feng, Y. (eds) Natural Language Processing and Chinese Computing. NLPCC 2013. Communications in Computer and Information Science, vol 400. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41644-6_21
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