Quality Evaluation of Product Reviews Using an Information Quality Framework
Date Issued
2009
Date
2009
Author(s)
Tseng, You-De
Abstract
The ubiquity of Web 2.0 makes the Internet an invaluable source of business information. For instance, product reviews composed collaboratively by many independent Internet reviewers can help consumers make purchase decisions and enable enterprises to improve their business strategies. As the number of reviews is increasing exponentially, opinion mining is needed to identify important reviews and opinions to answer users’ queries. Most opinion mining approaches try to extract sentimental or bipolar expressions from a large volume of reviews. However, the mining process often ignores the quality of each review and may retrieve useless or even noisy documents. In this thesis, we propose a method for evaluating the quality of information in product reviews. We treat the evaluation of review quality as a classification problem and employ an effective information quality framework to extract representative review features. Experiments based on an expert-composed data corpus demonstrate that the proposed method outperforms state-of-the-art approaches significantly. Moreover, this thesis implements detailed lift analyses to find the important factors for constructing high-quality reviews. Finally, we propose a prototype of review retrieval system that based on the classifier of review quality to help users to efficiently search the reviews that contain helpful information they want.
Subjects
text mining
classification
opinion mining
information quality
product reviews
support vector machine
review retrieval system
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