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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/IREV9I5-1712033
Abstract
This paper presents a machine learning?based system for detecting fake product reviews using Natural Language Processing (NLP) techniques. With the rapid growth of e-commerce, online reviews significantly influence consumer purchasing behavior, but the rise of deceptive or manipulated reviews has undermined their reliability. The proposed model utilizes text preprocessing methods such as tokenization, stop-word removal, and stemming, followed by feature extraction using TF-IDF and CountVectorizer. Multiple supervised learning algorithms, including Logistic Regression, Random Forest, Decision Tree, Na?ve Bayes, and K-Nearest Neighbors (KNN), were implemented to classify reviews as genuine or fake. Experimental results show that the Support Vector Machine (SVM) achieved the highest accuracy of approximately 88.5%, outperforming other models. Analysis of feature importance and confusion matrices revealed that linguistic and frequency-based attributes play a key role in deception detection. The developed system also includes a real-time review classification module, demonstrating its potential for application in Deceptive Review Detection, review moderation, and consumer trust enhancement.
Keywords
Fake Product Reviews, Machine Learning, Natural Language Processing (NLP), Text Classification, Deceptive Review Detection.
How to cite this paper
@article{1712033,
author = {Abhishek Gupta, Ayush Nigam, Abhishek Kumar, Dr. Ishrat Ali, Prof. (Dr.) Sanjay Pachauri},
title = {Automated Detection of Deceptive Online Product Reviews Using Supervised Learning Techniques},
journal = {Iconic Research And Engineering Journals},
year = {2025},
volume = {9},
number = {5},
pages = {887-889},
issn = {2456-8880},
url = {https://www.irejournals.com/formatedpaper/1712033.pdf},
abstract = {This paper presents a machine learning?based system for detecting fake product reviews using Natural Language Processing (NLP) techniques. With the rapid growth of e-commerce, online reviews significantly influence consumer purchasing behavior, but the rise of deceptive or manipulated reviews has undermined their reliability. The proposed model utilizes text preprocessing methods such as tokenization, stop-word removal, and stemming, followed by feature extraction using TF-IDF and CountVectorizer. Multiple supervised learning algorithms, including Logistic Regression, Random Forest, Decision Tree, Na?ve Bayes, and K-Nearest Neighbors (KNN), were implemented to classify reviews as genuine or fake. Experimental results show that the Support Vector Machine (SVM) achieved the highest accuracy of approximately 88.5%, outperforming other models. Analysis of feature importance and confusion matrices revealed that linguistic and frequency-based attributes play a key role in deception detection. The developed system also includes a real-time review classification module, demonstrating its potential for application in Deceptive Review Detection, review moderation, and consumer trust enhancement.},
keywords = {Fake Product Reviews, Machine Learning, Natural Language Processing (NLP), Text Classification, Deceptive Review Detection.},
month = {November},
doi = {https://doi.org/10.64388/IREV9I5-1712033}
}