Research on Semantic Orientation Classification of Chinese Online Product Reviews Based on Multi-aspect Sentiment Analysis

User-generated reviews on the e-commerce site reflect consumers' sentiment about products, which can further direct consumers' purchasing behaviors and sellers' marketing strategies. In this paper, we propose a semi-supervised approach to mine the aspects of product discussed in Chinese online reviews and also the sentiments expressed in different aspects. We first apply the Latent Dirichlet Allocation model to discover multi-aspect global topics of the product reviews, then extract the opinion short sentences based on sliding windows and pattern matching from context over the review text. The polarity of the associated sentiment is classified by the domain lexicon-based method. Finally the results are collected as features for the feedback for the machine learning method and applied in semantic orientation classification. The experiment results show that the novel method we proposed could help to discover multi-aspect fine-grained topics and associated sentiment, which helps to improve semantic orientation classification simultaneously.