  This paper presents a non-trivial reconstruction of a previous joint
topic-sentiment-preference review model TSPRA with stick-breaking
representation under the framework of variational inference (VI) and stochastic
variational inference (SVI). TSPRA is a Gibbs Sampling based model that solves
topics, word sentiments and user preferences altogether and has been shown to
achieve good performance, but for large data set it can only learn from a
relatively small sample. We develop the variational models vTSPRA and svTSPRA
to improve the time use, and our new approach is capable of processing millions
of reviews. We rebuild the generative process, improve the rating regression,
solve and present the coordinate-ascent updates of variational parameters, and
show the time complexity of each iteration is theoretically linear to the
corpus size, and the experiments on Amazon data sets show it converges faster
than TSPRA and attains better results given the same amount of time. In
addition, we tune svTSPRA into an online algorithm ovTSPRA that can monitor
oscillations of sentiment and preference overtime. Some interesting
fluctuations are captured and possible explanations are provided. The results
give strong visual evidence that user preference is better treated as an
independent factor from sentiment.
