Sentiment analysis for online reviews using an author-review-object model

Authors:
Yong Zhang;Dong-Hong Ji;Ying Su;Cheng Sun
Affiliations:
Computer School, Wuhan University, Wuhan, P.R. China;Computer School, Wuhan University, Wuhan, P.R. China;Department of Computer Science, Wuchang Branch, Huazhong University of Science and Technology, Wuhan, P.R. China;Computer School, Wuhan University, Wuhan, P.R. China
Venue:
AIRS'11 Proceedings of the 7th Asia conference on Information Retrieval Technology
Year:
2011

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Abstract

In this paper, we propose a probabilistic generative model for online review sentiment analysis, called joint Author-Review-Object Model (ARO). The users, objects and reviews form a heterogeneous graph in online reviews. The ARO model focuses on utilizing the user-review-object graph to improve the traditional sentiment analysis. It detects the sentiment based on not only the review content but also the author and object information. Preliminary experimental results on three datasets show that the proposed model is an effective strategy for jointly considering the various factors for the sentiment analysis.