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Issue No. 05 - September/October (2008 vol. 23)
ISSN: 1541-1672
pp: 42-49
Zhu Zhang , University of Arizona
The author identifies a new task in the ongoing research in text sentiment analysis: aggregating online product reviews in light of two orthogonal dimensions, namely, polarity/opinion extraction and usefulness scoring. The motivation is to build future review aggregation or ranking services that enable both online shoppers and vendors to better leverage information from multiple sources. Usefulness scoring is viewed as a regression problem. The author builds support-vector-regression models by incorporating a diverse set feature set computed from review text, which achieved promising performance on four Amazon product review collections. Findings also indicate that a product review's perceived usefulness is highly dependent on its linguistic style. Further rank correlation analyses on the Amazon data demonstrates the feasibility and advantage of the proposed review-aggregation framework, in the context of predicting market response to certain products. This article is part of a special issue on Natural Language Processing and the Web.
Information Search and Retrieval, Text mining, Web mining, Machine learning, Web text analysis

Z. Zhang, "Weighing Stars: Aggregating Online Product Reviews for Intelligent E-commerce Applications," in IEEE Intelligent Systems, vol. 23, no. , pp. 42-49, 2008.
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