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

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.

Keywords:

• Generative approach
• Joint modeling
• Latent Dirichlet allocation
• Service analytics
• Text mining

Supplementary Materials

Supplementary document: The pdf file contains: (i) derivations for the model inference in Section 3.3; (ii) extended experiments on Amazon datasets by considering domain-specific knowledge; (iii) and additional simulation results complementing Figure 5.

Code and data: A zip file named “JSTRRexp” contains codes and data to reproduce case study and simulation results in this article.