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


Zheng Chen, Yong Zhang, Yue Shang, Xiaohua Hu ;
Proceedings of The 8th Asian Conference on Machine Learning, PMLR 63:222-237, 2016.


This paper proposes a new HDP based online review rating regression model named Topic-Sentiment-Preference Regression Analysis (TSPRA). TSPRA combines topics (i.e. product as-pects), word sentiment and user preference as regression factors, and is able to perform topic clus-tering, 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. Our experiments conducted on 22 Amazon datasets show overwhelming better performance in rating predication against a state-of-art model FLAME (2015) in terms of error, Pearson’s Correlation and number of inverted pairs. For sentiment analysis, we compare the derived word sentiments against a public sentiment resource SenticNet3 and our sentiment estimations clearly make more sense in the context of 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 prod-uct aspects seriously concerned by users but negatively commented in reviews. Improvement to such “critical aspects” could be most effective to enhance user experience.

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