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Analyzing and Assessing Reviews on



Reviews are contents written by users to express opinions on products or services. The information contained in reviews is valuable to users who are going to make decisions on products or services. However, there are numbers of reviews for popular products, and the quality of reviews is not always good. It’s necessary to pick out reviews, which are in high quality from numbers of reviews to assist user in making decision. In this paper, we collected 21,501 reviews flagged as good from 499,253 products on We observed the level of users is an important factor affects the quality of reviews, and users prefer to post short reviews containing the description of the quality and price of the product. We proposed a system to assess the quality of reviews automatically in this paper. We achieved that by applying SVM classification based on two kinds of features; reviews and reviewers that would help users find out high quality reviews and useful information from massive reviews. We evaluated our system on The accuracy of our experiments for reviews quality assessing reached to 87.5 percent.



Total Pages: 7
Pages: 73-80


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Volume: 24
Issue: 1
Year: 2018

Cite this document


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ISSN PRINT: 1079-8587
ISSN ONLINE: 2326-005X
DOI PREFIX: 10.31209
10.1080/10798587 with T&F
IMPACT FACTOR: 0.652 (2017/2018)

SJR: "The two years line is equivalent to journal impact factor ™ (Thomson Reuters) metric."

Journal: 1995-Present


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