Detecting fake reviews through topic modelling

OzturkBirim, Sule, Kazancoglu, Ipek, Mangla, Sachin Kumar, Kahraman, Aysun, Kumar, Satish and Kazancoglu, Yigit (2022) Detecting fake reviews through topic modelling. Journal of Business Research, 149. pp. 884-900. ISSN 1873-7978

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Against the uncertainty caused by the information overload in the online world, consumers can benefit greatly by reading online product reviews before making their online purchases. However, some of the reviews are written deceptively to manipulate purchasing decisions. The purpose of present study is to determine which feature combination is most effective in fake review detection among the features of sentiment scores, topic distributions, cluster distributions and bag of words. In this study, additional feature combinations to a sentiment analysis are searched to examine the critical problem of fake reviews made to influence the decision-making process using review from dataset. Results of the study points that behavior-related features play an important role in fake review classifications when jointly used with text-related features. Verified purchase is the only behavior related feature used comparatively with other text-related features.

Item Type: Article
Keywords: Machine learning techniques | Fake online reviews | Natural language processing (NLP) | Online retailing | Purchasing decision
Subjects: Social Sciences and humanities > Business, Management and Accounting > Management of Technology and Innovation
JGU School/Centre: Jindal Global Business School
Depositing User: Amees Mohammad
Date Deposited: 15 Jun 2022 09:27
Last Modified: 02 Jul 2022 10:19
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