Clustering For Mining a Product Purchasing or verifying Online
Author(s):
V.sheshadri, Dr.K.Venkataramana
Keywords:
Social Media, Social Network, Spammer, Spam Review, Fake Review, Heterogeneous Information Networks.
Abstract
Nowadays, a giant a part of individuals have confidence available content in social media in their choices (e.g. reviews and feedback on a subject or product). the likelihood that anybody will leave a review offer a golden chance for spammers to write down spam reviews concerning product and services for various interests. Identifying these spammers and also the spam content may be a hot topic of analysis and though a substantial variety of studies have been done recently toward this finish, however thus far the methodologies put forth still barely find spam reviews, and none of them show the importance of every extracted feature sort. In this study, we propose a unique framework that utilizes spam options for modeling review datasets as heterogeneous information networks to map spam detection procedure into a classification downside in such networks. mistreatment the importance of spam options facilitate the United States to get higher leads to terms of different metrics experimented on real-world review datasets from Yelp and Amazon websites. The results show that our project outperforms the present strategies and among four classes of features; together with review-behavioral, user-behavioral, review linguistic, user-linguistic, the primary form of options performs higher than the opposite classes.
Article Details
Unique Paper ID: 145606

Publication Volume & Issue: Volume 4, Issue 10

Page(s): 622 - 625
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