International Journal of Innovative Research in Computer and Communication Engineering
ISSN Approved Journal | Impact factor: 8.771 | ESTD: 2013 | Follows UGC CARE Journal Norms and Guidelines
| Monthly, Peer-Reviewed, Refereed, Scholarly, Multidisciplinary and Open Access Journal | High Impact Factor 8.771 (Calculated by Google Scholar and Semantic Scholar | AI-Powered Research Tool | Indexing in all Major Database & Metadata, Citation Generator | Digital Object Identifier (DOI) |
| TITLE | AI System to Detect Fake Product Reviews in E-Commerce |
|---|---|
| ABSTRACT | Customer reviews are crucial for e-commerce platforms because they establish the legitimacy of products and sway consumers' decisions to buy. However, by disseminating false information and undermining consumer trust, the growing number of phony or manipulated reviews has presented significant difficulties. The goal of this research is to create an artificial intelligence (AI)-based system that uses cutting-edge machine learning (ML) and natural language processing (NLP) techniques to automatically identify fraudulent product reviews. To find misleading or biased content, the suggested system examines textual patterns, sentiment polarity, reviewer history, and review frequency. The system seeks to improve transparency, guarantee fair competition among sellers, and boost consumer confidence in online marketplaces by eliminating reviews that aren't trustworthy. Finally, by incorporating AI-driven solutions for real-time fake review detection, this study helps to improve the integrity and dependability of e-commerce platforms. |
| AUTHOR | SANTOSH GOPALE, SWATI MAHAJAN, DATTATRAY CHAUDHARI, KOMAL CHAUDHARI |
| PUBLICATION DATE | 2025-11-06 |
| VOLUME | 176 |
| DOI | DOI: 10.15680/IJIRCCE.2025.1311008 |
| pdf/8_AI System to Detect Fake Product Reviews in E-Commerce.pdf | |
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