A Kernel-Based Sentiment Classification Approach for Chinese Sentences

There has been a large growth of online opinioned customer reviews in the recent years. Classifying such reviews into polarized ones would be beneficial in business intelligence and other application domains. This paper aims at finding a solution for the sentiment classification at a fine-grained level, namely the sentence level. The challenge is that because a sentiment expression is more free-style, it is more difficult to determine classification features. Therefore, we propose a kernel-based machine learning approach to make it feasible for incorporating multiple features from lexical and syntactic levels. The experiment results have shown that our approach is effective and outperforms the very competitive n-gram method.