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Analysis of online reviews has attracted great attention with broadapplications. Often times, the textual reviews are coupled with the numericalratings in the data. In this work, we propose a probabilistic model toaccommodate both textual reviews and overall ratings with consideration oftheir intrinsic connection for a joint sentiment-topic prediction. The key ofthe proposed method is to develop a unified generative model where the topicmodeling is constructed based on review texts and the sentiment prediction isobtained by combining review texts and overall ratings. The inference of modelparameters are obtained by an efficient Gibbs sampling procedure. The proposedmethod can enhance the prediction accuracy of review data and achieve aneffective detection of interpretable topics and sentiments. The merits of theproposed method are elaborated by the case study from Amazon datasets andsimulation studies.

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