In this chapter, we study the problem of mining sentiment information from online resources and investigate ways to use such information to predict product sales performance. In particular, we conduct an empirical study on using the sentiment information mined from blogs to predict movie box office performance. We propose Sentiment PLSA (S-PLSA), in which a blog entry is viewed as a document generated by a number of hidden sentiment factors. Training an S-PLSA model on the blog data enables us to obtain a succinct summary of the sentiment information embedded in the blogs. We then present ARSA, an autoregressive sentiment-aware model, to utilize the sentiment information captured by S-PLSA for predicting product sales performance. Extensive experiments were conducted on the movie data set. Experiments confirm the effectiveness and superiority of the proposed approach.
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