[This article belongs to Volume - 54, Issue - 02]
Gongcheng Kexue Yu Jishu/Advanced Engineering Science
Journal ID : AES-28-12-2022-606

Title : A COMPARATIVE ANALYSIS OF DL APPROACHES USING FEATURE EXTRACTION FOR THE IDENTIFICATION OF FAKE REVIEWS
Vikas Attri, Isha Batra, Arun Malik

Abstract :

For third-party businesses, millions of reviews are shared on platforms such as Amazon (e-commerce), Airbnb (travel and hospitality), OYO (hotels), and Google. Because reviews have a great influence on consumers, spammers use Fake reviews to promote their products, services, or organizations while demoting competitors. Several researchers have presented various methods for detecting bogus reviews. This research focuses on detecting false reviews using deep learning approaches. The research is broken into 2 sections: the first half examines ML methods and the reasons for preferring deep learning over machine learning. The second section of the study includes a systematic review of deep learning algorithms, summarizing these techniques in a tabular format, as well as the current research gaps in the relevant research field.