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Automated Detection of Deceptive Online Product Reviews Using Supervised Learning Techniques
Subject area: Science,Engineering and Technology · Area of research: Automated Detection and Supervised Learning
DOI: https://doi.org/10.64388/IREV9I10-1716505
Abstract
The rapid proliferation of e-commerce platforms has made online product reviews a central determinant of consumer purchasing behavior. However, the emergence of deceptive or fake reviews—generated by bots, paid agents, or competitors—significantly threatens the reliability of such feedback. This paper presents a comprehensive machine learning–based system for the automated detection of fake product reviews leveraging Natural Language Processing (NLP) techniques. Using a balanced dataset of 40,432 reviews (20,216 genuine labeled CG; 20,216 deceptive labeled OR) spanning 10 Amazon product categories, the proposed system applies a structured NLP pipeline comprising tokenization, stop-word removal, stemming, and lemmatization for text normalization. Feature extraction is performed using Bag-of-Words (CountVectorizer) and TF-IDF representations. Six supervised classification algorithms—Logistic Regression, Random Forest, Decision Tree, Naïve Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—are systematically trained, evaluated, and compared. Experimental results demonstrate that SVM achieves the highest classification accuracy of 88.5%, significantly outperforming all other models. Comprehensive evaluation using accuracy, precision, recall, and F1-score metrics confirms the robustness of the proposed framework. The study also includes a real-time review classification module, demonstrating its readiness for integration into live e-commerce moderation pipelines.
Keywords
Fake Product Reviews, Machine Learning, Natural Language Processing (NLP), Text Classification, Support Vector Machine, Deceptive Review Detection, TF-IDF, Sentiment Analysis, E-Commerce Trust
How to cite this paper
@article{1716505,
author = {Abhishek kumar, Abhsishek Gupta, Dr. Mohd Danish, Prof. (Dr.) Sanjay Pachauri},
title = {Automated Detection of Deceptive Online Product Reviews Using Supervised Learning Techniques},
journal = {Iconic Research And Engineering Journals},
year = {2026},
volume = {9},
number = {10},
pages = {1966-1971},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1716505.pdf},
abstract = {The rapid proliferation of e-commerce platforms has made online product reviews a central determinant of consumer purchasing behavior. However, the emergence of deceptive or fake reviews—generated by bots, paid agents, or competitors—significantly threatens the reliability of such feedback. This paper presents a comprehensive machine learning–based system for the automated detection of fake product reviews leveraging Natural Language Processing (NLP) techniques. Using a balanced dataset of 40,432 reviews (20,216 genuine labeled CG; 20,216 deceptive labeled OR) spanning 10 Amazon product categories, the proposed system applies a structured NLP pipeline comprising tokenization, stop-word removal, stemming, and lemmatization for text normalization. Feature extraction is performed using Bag-of-Words (CountVectorizer) and TF-IDF representations. Six supervised classification algorithms—Logistic Regression, Random Forest, Decision Tree, Naïve Bayes, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM)—are systematically trained, evaluated, and compared. Experimental results demonstrate that SVM achieves the highest classification accuracy of 88.5%, significantly outperforming all other models. Comprehensive evaluation using accuracy, precision, recall, and F1-score metrics confirms the robustness of the proposed framework. The study also includes a real-time review classification module, demonstrating its readiness for integration into live e-commerce moderation pipelines.},
keywords = {Fake Product Reviews, Machine Learning, Natural Language Processing (NLP), Text Classification, Support Vector Machine, Deceptive Review Detection, TF-IDF, Sentiment Analysis, E-Commerce Trust},
month = {April},
doi = {https://doi.org/10.64388/IREV9I10-1716505}
}