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Issue No. 01 - January-March (2011 vol. 2)
ISSN: 1949-3045
pp: 37-49
Jingbo Zhu , Northeastern University, Shenyang
Huizhen Wang , Northeastern University, Shenyang
Muhua Zhu , Northeastern University, Shenyang
Benjamin K. Tsou , Hong Kong Institute of Education, Hong Kong
Matthew Ma , Scientific Works, Princeton Junction
Opinion polling has been traditionally done via customer satisfaction studies in which questions are carefully designed to gather customer opinions about target products or services. This paper studies aspect-based opinion polling from unlabeled free-form textual customer reviews without requiring customers to answer any questions. First, a multi-aspect bootstrapping method is proposed to learn aspect-related terms of each aspect that are used for aspect identification. Second, an aspect-based segmentation model is proposed to segment a multi-aspect sentence into multiple single-aspect units as basic units for opinion polling. Finally, an aspect-based opinion polling algorithm is presented in detail. Experiments on real Chinese restaurant reviews demonstrated that our approach can achieve 75.5 percent accuracy in aspect-based opinion polling tasks. The proposed opinion polling method does not require labeled training data. It is thus easy to implement and can be applicable to other languages (e.g., English) or other domains such as product or movie reviews.
Opinion polling, sentiment analysis, opinion mining, aspect-based analysis.

H. Wang, B. K. Tsou, M. Zhu, J. Zhu and M. Ma, "Aspect-Based Opinion Polling from Customer Reviews," in IEEE Transactions on Affective Computing, vol. 2, no. , pp. 37-49, 2011.
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