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

TL;DR

This paper introduces TSPRA, an HDP-based model that unifies topic detection, sentiment analysis, and user preference evaluation in online reviews, improving rating prediction and enabling critical aspect identification.

Contribution

The paper presents a novel HDP-based framework that integrates user preference, sentiment, and topics, extending existing models by decoupling preference and sentiment for better analysis.

Findings

TSPRA outperforms FLAME in rating prediction accuracy.

Derived sentiments align well with SenticNet3.

Identifies critical aspects negatively impacting user experience.

Abstract

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 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. 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 product aspects seriously concerned by users but negatively commented in reviews. Improvement to such "critical aspects" could be most effective to enhance user experience.