Kernel-based Sentiment Classification for Chinese Sentence

Recent years have seen a large growth in the online customer reviews. Classifying these reviews into positive or negative ones would be helpful in business intelligence applications and recommender systems. This paper aims to solve the sentiment classification at a fine-grained level, i.e. the sentence level. The challenging aspect of this problem that distinguishes it from the traditional classification problem is that sentiment expression is more free-style. Classification features are more difficult to determine. In this paper, we propose a kernel-based method to make it is feasible for incorporating multiple features from word, n-gram and syntactic levels. Experiment results show that our method is effective, and it outperforms the very competitive n-gram method.