{"title":"Review ranking method for spam recognition","authors":"Gunjan Ansari, Tanvir Ahmad, M. Doja","doi":"10.1109/IC3.2016.7880240","DOIUrl":null,"url":null,"abstract":"E-commerce websites are becoming popular among customers who are buying products online. Online reviews play a major role in selling of online products. Reviews give the customer a complete overview of the product thus making it popular or unpopular among buyers thus increasing its sales. In order to increase sales of a product, reviewers are writing fake reviews. In this paper, a review ranking method is proposed. This method assigns a score to each review based on different parameters. The reviews having high score are considered to be more helpful or genuine and thus are ranked higher than the reviews having lower score. The lower ranked reviews are fake reviews and thus they are non-useful to the users. The proposed approach is an effective approach which avoids heavy computation of learning. Evaluation on real-life flipkart review dataset shows a precision of 83.3% thus showing the effectiveness of proposed model.","PeriodicalId":294210,"journal":{"name":"2016 Ninth International Conference on Contemporary Computing (IC3)","volume":"24 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 Ninth International Conference on Contemporary Computing (IC3)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/IC3.2016.7880240","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 2
Abstract
E-commerce websites are becoming popular among customers who are buying products online. Online reviews play a major role in selling of online products. Reviews give the customer a complete overview of the product thus making it popular or unpopular among buyers thus increasing its sales. In order to increase sales of a product, reviewers are writing fake reviews. In this paper, a review ranking method is proposed. This method assigns a score to each review based on different parameters. The reviews having high score are considered to be more helpful or genuine and thus are ranked higher than the reviews having lower score. The lower ranked reviews are fake reviews and thus they are non-useful to the users. The proposed approach is an effective approach which avoids heavy computation of learning. Evaluation on real-life flipkart review dataset shows a precision of 83.3% thus showing the effectiveness of proposed model.
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