User-aware topic modeling of online reviews

The online reviews are one type of social media which are opinions generated by the users to comment on some special items. Since the sentiments are dependent on topics, probabilistic topic models have been widely used for sentiment analysis. However, most of existing methods only model the text, but rarely consider the users, who express the opinions, and the items, which the opinions are expressed on. Different users are usually concerned with different topics and use different sentiment expressions, a lenient user might tend to give positive review than a critical user. High-quality items tend to receive positive reviews than low-quality items. To better model the topics and sentiments, we argue that it is essential to explore reviews as well as users and items. To this end, we propose a novel model called User Item Sentiment Topic (UIST) which incorporates users and items for topic modeling and produces topic–word, user–topic, and item–topic distributions simultaneously. Extensive experiments on several datasets demonstrate the advantages and effectiveness of our method. The extracted topics with our method are more coherent and informative; consequently, the performance of sentiment classification is also improved. Furthermore, the user preference obtained with our method could be utilized for many personalized applications.