On-line evolutionary sentiment topic analysis modeling

Abstract

As the rapid booming of reviews, a valid sentiment analysis model will significantly boost the review recommendation system’s capability, and present more constructive information for consumers. Topic probabilistic models have already shown many advantages for detecting potential structure of topics and sentiments in reviews corpus. However, most reviews are presented through time-dependent data streams and some respects of the potential structure are unfixed and time-varying, such as topic number and word probability distribution. In this paper, a novel probabilistic topic modelling framework is proposed, called on-line evolutionary sentiment/topic modeling (OESTM), which has the capacity for achieving the optimization of the aforementioned aspects. Firstly, OESTM depends on an improved non-parametric Bayesian model for estimating the best number of topics that can perfectly explain the current time-slice, and analyzes these latent topics and sentiment polarities simultaneously. Secondly, OESTM implements the birth, death and inheritance for detected topics through the transfer of parameters from previous time slices to the updated time slice. The experiments show that significant improvements have been achieved by the proposed model with respect to other state-of-the-art models.