An Adaptive Model for Probabilistic Sentiment Analysis

Online reviews, which are getting increasingly prevalent with the rapid growth of Web 2.0, have been shown to be second only to ``word-of-mouth'' in terms of influencing purchase decisions. It is therefore imperative to analyze them and distill useful knowledge that could be of economic values to vendors and other interested parties. Previous studies have confirmed that the sentiments expressed in the online reviews are strongly correlated with the sales performance of products. In particular, a model called ARSA has been proposed for predicting sales performance using a model called S-PLSA. In this paper, we build upon that work, and present an adaptive sentiment analysis model called S-PLSA+, which not only can capture the hidden sentiment factors in the reviews, but has the capability to be incrementally updated as more data become available. We show how the proposed S-PLSA+ model can be applied to sales performance prediction using the ARSA model. A case study is conducted in the movie domain, and results from preliminary experiments confirm the effectiveness of the proposed model.