Unifying Topic, Sentiment & Preference in an HDP-Based Rating Regression Model for Online Reviews

This paper proposes a new HDP based online review rating regression model named TopicSentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (product aspects), word sentiment and user preference as regression factors, and is able to perform topic clustering, review rating prediction, sentiment analysis and what we invent as ”critical aspect” analysis altogether in one framework. TSPRA extends sentiment approaches by integrating the key concept ”user preference” in collaborative filtering (CF) models into consideration, while it is distinct from current CF models by decoupling ”user preference” and ”sentiment” as independent factors. ”Critical aspects” is defined as the product aspects seriously concerned by users but negatively commented in reviews. Improvement to such ”critical aspects” could be most effective to enhance user experience.