A Customized Dependency Tree Kernel for Effective Sentiment Classification

This paper introduces a kernel over dependency trees for sentiment classification. In order to classify a text as positive or negative, syntactic and word dependency information should be exploited besides words. Dependency parse trees, generated by automatic sentence parser, contain much syntactic information which would be helpful for sentiment classification. On the other hand, dependency trees contain word dependency information of sentences, and give a deeper understanding of natural language than BOW (bag-of-word) and n-gram schemas. In this paper, we present an approach which exploits such syntactic and word dependency information for sentiment classification. Our approach achieves good performance. We also compared the dependency tree kernel we proposed with some other tree kernels.