Fine-grained opinion mining by integrating multiple review sources

Authors:
Qingliang Miao;Qiudan Li;Daniel Zeng
Affiliations:
Institute of Automation, Chinese Academy of Sciences, Beijing, China;Institute of Automation, Chinese Academy of Sciences, Beijing, China;MIS Department, University of Arizona, Tucson, AZ
Venue:
Journal of the American Society for Information Science and Technology
Year:
2010

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Abstract

With the rapid development of Web 2.0, online reviews have become extremely valuable sources for mining customers' opinions. Fine-grained opinion mining has attracted more and more attention of both applied and theoretical research. In this article, the authors study how to automatically mine product features and opinions from multiple review sources. Specifically, they propose an integration strategy to solve the issue. Within the integration strategy, the authors mine domain knowledge from semistructured reviews and then exploit the domain knowledge to assist product feature extraction and sentiment orientation identification from unstructured reviews. Finally, feature-opinion tuples are generated. Experimental results on real-world datasets show that the proposed approach is effective. © 2010 Wiley Periodicals, Inc.