Abstract：In this paper, we study how to apply machine learning techniques to solve sentiment classification problems. The main task of sentiment classification is to determine whether news or reviews is negative or positive. Naive Bayes and Maximum Entropy classification are used for the sentiment classification of Chinese news and reviews. The experimental results show that the methods we employed perform well. The accuracy of classification can achieve about 90%. Moreover, we find that selecting the words with polarity as features, negation tagging and representing test documents as feature presence vectors can improve the performance of sentiment classification. Conclusively, sentiment classification is a more challenging problem.
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2007, Vol. 21 
