Sentiment Classification Based on Syntax Tree Pruning and Tree Kernel

Abstract

Sentiment classification is a way to analyze the subjective information in the text and then mine the opinion. We focus on the sentence-level sentiment classification. On the systematically analyzing the importance and difficulties of the sentence-level sentiment classification, this paper proposes a syntax tree pruning and tree kernel-based approach to sentiment classification. In our method, the convolution kernel of SVM is first used to obtain structured information, and then apply syntax tree as a feature in Sentiment Classification. Firstly, we focus on how to apply the structured features from the syntax tree to the sentiment classification and propose a novel approach of sentence-level sentiment classification which apply the tree kernel and composite kernel to the SVM classifier. Secondly, we provide two kinds of syntax tree pruning strategies: adjectives-based and sentiment words-based. The experimental results show that our method can achieve better performance in sentence level Sentiment Classification.