Paper Title
Fake Review Detection And Analysis
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Registration ID: IJNRD_305232
Published ID: IJNRD2504108
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Keywords
Natural Language Processing (NLP), Sentiment Analysis, Supervised Learning, Unsupervised Learning.
Abstract
In this research paper, we explore the application of Machine Learning (ML) techniques for detecting fake reviews on e-commerce platforms. Online shopping has transformed consumer behavior, providing convenience and accessibility. However, the widespread presence of deceptive reviews has undermined trust, manipulated product ratings, and influenced purchasing decisions, posing a significant challenge to market fairness. Therefore, identifying and filtering out fake reviews has become essential for maintaining credibility and ensuring a reliable shopping experience. This paper examines various ML models, including supervised learning approaches such as Decision Trees and Natural Language Processing (NLP)-based sentiment analysis, to detect fraudulent reviews. Our proposed system leverages metadata tracking—such as IP addresses, MAC addresses, device details, and review frequency—to identify suspicious activities and detect anomalies. Additionally, the system integrates text duplication detection and behavioral footprint analysis to enhance the accuracy of classification. The model is implemented using Pandas for data processing, Scikit-learn for machine learning algorithms, and Flask for deployment, ensuring efficient real-time detection of fake reviews. An admin panel is incorporated to monitor reviews, manage product listings, and eliminate deceptive feedback. Users can browse products, submit reviews, and place orders while AI-driven analysis verifies the authenticity of submitted feedback. Experimental results demonstrate that the proposed approach achieves high precision and recall, significantly outperforming traditional rule-based detection methods. Future enhancements include deep learning techniques such as BERT and LSTMs, real-time fraud detection, multi-language support, and blockchain integration to improve transparency and trust. This research aims to create a secure and credible e-commerce ecosystem by mitigating fraudulent activities and fostering consumer confidence in online marketplaces.
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How To Cite (APA)
Suraj Kale, Amit Godale, Dikshant Koriwar, Kaushal Rekhe, & Umesh Nikam (April-2025). Fake Review Detection And Analysis . INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 10(4), a912-a918. https://ijnrd.org/papers/IJNRD2504108.pdf
Issue
Volume 10 Issue 4, April-2025
Pages : a912-a918
Other Publication Details
Paper Reg. ID: IJNRD_305232
Published Paper Id: IJNRD2504108
Research Area: Science and Technology
Author Type: Indian Author
Country: Amravati, Maharashtra, India
Published Paper PDF: https://ijnrd.org/papers/IJNRD2504108.pdf
Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2504108
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