Dependency-Topic-Affects-Sentiment-LDA Model for Sentiment Analysis

Abstract

Sentiment analysis tends to use automated approaches to mine the sentiment information expressed in text, such as reviews, blogs and forum discussions. As most traditional approaches for sentiment analysis are based on supervised learning models and need many labeled corpora as their training data which are not always easily obtained, various unsupervised models based on Latent Dirichlet Allocation (LDA) have been proposed for sentiment classification. In this paper, we propose a novel probabilistic modeling framework based on LDA, called Dependency-Topic-Affects-Sentiment-LDA (DTAS) model, which drops the "bag of words" assumption and assumes that the topics of sentences in a document form a Markov chain, and the sentiment of one sentence is affected by its corresponding topic and its previous sentence's topic. We applied DTAS to reviews of books and hotels. The experiment results of sentiment classification shows that DTAS outperforms other unsupervised generative models and gets high and stable accuracy.