S-PLASA+: adaptive sentiment analysis with application to sales performance prediction

Authors:
Yang Liu;Xiaohui Yu;Xiangji Huang;Aijun An
Affiliations:
Shandong University, Jinan, China;York University, Toronto, ON, Canada;York University, Toronto, ON, Canada;York University, Toronto, ON, Canada
Venue:
Proceedings of the 33rd international ACM SIGIR conference on Research and development in information retrieval
Year:
2010

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Abstract

Analyzing the large volume of online reviews would produce useful knowledge that could be of economic values to vendors and other interested parties. In particular, the sentiments expressed in the online reviews have been shown to be strongly correlated with the sales performance of products. In this paper, we present an adaptive sentiment analysis model called S-PLSA+, which aims to capture the hidden sentiment factors in the reviews with the capability to be incrementally updated as more data become available. We show how S-PLSA+ can be applied to sales performance prediction using an ARSA model developed in previous literature. A case study is conducted in the movie domain, and results from preliminary experiments confirm the effectiveness of the proposed model.