Sentiment Classification Based on AS-LDA Model

Abstract

We address the task of sentiment classification - identification of the polarity of the subjective document in this paper. We introduces a sentiment classification method called AS LDA. In this model, we assume that words in subjective documents consists of two parts: sentiment element words and auxiliary words which are sampled accordingly from sentiment topics and auxiliary topics. Sentiment element words include targets of the opinions, polarity words and modifiers of polarity words. Experimental results demonstrate that our approach outperforms Latent Dirichlet Allocation (LDA).