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Title:
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AN APPROACH FOR PREDICTING AND RANKING CONSUMER REVIEW HELPFULNESS |
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Author(s):
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Richong Zhang , Thomas Tran |
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ISBN:
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978-972-8924-66-9 |
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Editors:
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Piet Kommers, Pedro Isaías and Nian-Shing Chen |
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Year:
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2008 |
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Edition:
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Single |
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Keywords:
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Consumer review, Ranking, Helpfulness, Electronic commerce |
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Type:
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Full Paper |
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First Page:
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128 |
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Last Page:
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135 |
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Language:
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English |
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Cover:
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Full Contents:
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click to dowload 
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Paper Abstract:
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This article proposes an approach to help consumers find helpful online product reviews. With the growth of the Internet,
more and more consumers would like to share their opinions on this media. Intermediator web sites, such as
Amazon.com, provide platforms for consumers to review products and retailers. However, it is impossible for consumers
to read through the huge amount of reviews. Also, the quality and the helpfulness of reviews is unavailable before
consumers read those reviews. In this paper, opinions from online communities are ranked, and reviews that may help
consumers better than others will be found. The reviews crawled from Amazon.com are analyzed and ranked by our
scoring model. The experimental results show that our approach is effective in ranking and classifying small set of online
reviews. With the navigation of our approach, consumers can find helpful reviews faster and make the decision easier. |
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