Multi-aspect sentiment analysis for Chinese online social reviews based on topic modeling and HowNet lexicon

User-generated reviews on the Web reflect users’ sentiment about products, services and social events. Existing researches mostly focus on the sentiment classification of the product and service reviews in document level. Reviews of social events such as economic and political activities, which are called social reviews, have specific characteristics different to the reviews of products and services. In this paper, we propose an unsupervised approach to automatically discover the aspects discussed in Chinese social reviews and also the sentiments expressed in different aspects. The approach is called Multi-aspect Sentiment Analysis for Chinese Online Social Reviews (MSA-COSRs). We first apply the Latent Dirichlet Allocation (LDA) model to discover multi-aspect global topics of social reviews, and then extract the local topic and associated sentiment based on a sliding window context over the review text. The aspect of the local topic is identified by a trained LDA model, and the polarity of the associated sentiment is classified by HowNet lexicon. The experiment results show that MSA-COSR cannot only obtain good topic partitioning results, but also help to improve sentiment analysis accuracy. It helps to simultaneously discover multi-aspect fine-grained topics and associated sentiment.