Using Data Science for the Analysis of Fake Review Detection on E-Commerce Websites

Aaruksha Dahiya

Volume 6, Issue 2 2022

Page: 12-15

Abstract

Client surveys are fundamental in impacting buying choices on web-based business sites, which are becoming progressively well-known for web-based shopping. The presence of fake reviews, then again, could affect the validity and trustworthiness of these stages. Thus, counterfeit audit recognizable proof has created a critical report field, with AI, computerized reasoning, and information science procedures arising as promising ways to settle this issue. This survey paper presents a total outline of the latest techniques for identifying false surveys on webbased business sites, zeroing in on AI, artificial intelligence, and information science. We assess the convenience of a few methodologies in recognizing misleading surveys, including based, conduct-based, and profound learning-based strategies. We additionally examine the snags and future bearings in counterfeit audit location research, including imbalanced datasets, ill-disposed assaults, multimodal fake reviews, ongoing location, reasonableness, moral ramifications, and area information joining. This survey article aims to outline the current examination climate in bogus survey recognizable proof on web-based business sites using AI, artificial intelligence, and information science and guide future exploration around here.

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