JST-RR Model: Joint Modeling of Ratings and Reviews in Sentiment-Topic Prediction

TL;DR

This paper introduces a probabilistic model that jointly analyzes review texts and ratings to improve sentiment-topic prediction accuracy and interpretability in online review data.

Contribution

It presents a unified generative model combining textual reviews and ratings, with an efficient Gibbs sampling inference method, advancing joint sentiment-topic analysis.

Findings

Improved prediction accuracy on Amazon review datasets

Effective detection of interpretable topics and sentiments

Validated through case studies and simulation studies

Abstract

Analysis of online reviews has attracted great attention with broad applications. Often times, the textual reviews are coupled with the numerical ratings in the data. In this work, we propose a probabilistic model to accommodate both textual reviews and overall ratings with consideration of their intrinsic connection for a joint sentiment-topic prediction. The key of the proposed method is to develop a unified generative model where the topic modeling is constructed based on review texts and the sentiment prediction is obtained by combining review texts and overall ratings. The inference of model parameters are obtained by an efficient Gibbs sampling procedure. The proposed method can enhance the prediction accuracy of review data and achieve an effective detection of interpretable topics and sentiments. The merits of the proposed method are elaborated by the case study from Amazon datasets and simulation studies.