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Bimonthly Since 1986 |
ISSN 1004-9037
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Publication Details |
Edited by: Editorial Board of Journal of Data Acquisition and Processing
P.O. Box 2704, Beijing 100190, P.R. China
Sponsored by: Institute of Computing Technology, CAS & China Computer Federation
Undertaken by: Institute of Computing Technology, CAS
Published by: SCIENCE PRESS, BEIJING, CHINA
Distributed by:
China: All Local Post Offices
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09 May 2023, Volume 38 Issue 3
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Abstract
Over the last decade, online marketplaces are evolving at a high rate and customers are relying more on the internet for their purchases. A more positive and favourable review about a product or service draws more customers, generating more profit. At the same time, Reviews without experience with the product have also been written to attract customers. Hence, spotting these bogus reviews is a crucial and important research field. The capacity to recognize fake reviews is influenced by both the review's fundamental elements and the reviewers' actions. Extensive research has been done in this area to identify fraudulent reviews and reviewers are generally involved in this. However, the group of bogus reviewers works together as a team to concentrate on particular products and submit false reviews of those products in large numbers. The goal of this work is to present a robust, thorough comparative and comprehensive analysis for applying machine learning to identify false reviews and reviewers.
Keyword
Fake review, Fake reviewers, Spam opinion, feature engineering.
PDF Download (click here)
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