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This paper proposes a new HDP based online review rating regression modelnamed Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combinestopics (i.e. product aspects), word sentiment and user preference as regressionfactors, and is able to perform topic clustering, review rating prediction,sentiment analysis and what we invent as "critical aspect" analysis altogetherin one framework. TSPRA extends sentiment approaches by integrating the keyconcept "user preference" in collaborative filtering (CF) models intoconsideration, while it is distinct from current CF models by decoupling "userpreference" and "sentiment" as independent factors. Our experiments conductedon 22 Amazon datasets show overwhelming better performance in ratingpredication against a state-of-art model FLAME (2015) in terms of error,Pearson's Correlation and number of inverted pairs. For sentiment analysis, wecompare the derived word sentiments against a public sentiment resourceSenticNet3 and our sentiment estimations clearly make more sense in the contextof online reviews. Last, as a result of the de-correlation of "user preference"from "sentiment", TSPRA is able to evaluate a new concept "critical aspects",defined as the product aspects seriously concerned by users but negativelycommented in reviews. Improvement to such "critical aspects" could be mosteffective to enhance user experience.

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