  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.
