A comparison study of multi-class sentiment classification for Chinese reviews

Most of the previous researches on sentiment analysis concentrate on the binary distinction of positive vs. negative. This paper presents the multi-class sentiment classification problem that attempt to mine the implied rating information from reviews. We use four machine learning methods and two feature selection methods to find out whether or not the multi-class sentiment classification problem is the same to the binary sentiment classification problem, and whether it is equal to the traditional multi-class classification problem. Experiments show that multi-class sentiment classification problem is difficult than that of only determining the polarity of a review and that it is different from traditional multi-class classification problem, thus traditional multi-class classification method can not be directly used to deal with this problem.