Chinese Microblogs Sentiment Classification using Maximum Entropy

This paper presents our Chinese microblog sentiment classification (CMSC) system in the Topic-Based Chinese Message Polarity Classification task of SIGHAN-8 Bake-Off. Given a message from Chinese Weibo platform and a topic, our system is designed to classify whether the message is of positive, negative, or neutral sentiment towards the given topic. Due to the difficulties like the out-ofvocabulary Internet words and emoticons, polarity classification of Chinese microblogs is still an open problem today. In our system, Maximum Entropy (MaxEnt) is employed, which is a discriminative model that directly models the class posteriors, allowing them to incorporate a rich set of features. Moreover, oversampling approach is used to hand the unbalance problem. Evaluation results demonstrate the utility of our system, showing an accuracy of 66.4% for restricted resource and 66.6% for unrestricted resource.