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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
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.
References
[1] Ahmad, A., & Siddiqui, M. F. (2022). Detecting deceptive online reviews using machine learning and NLP techniques. Journal of Information and Computational Science, 12(5), 45–53.
[2] Banerjee, S., & Choudhary, A. (2021). Fake review detection using natural language processing and supervised learning approaches. International Journal of Data Science and Analytics, 9(4), 315–327.
[3] Mukherjee, A., Venkataraman, V., Liu, B., & Glance, N. (2013). What Yelp fake review filter might be doing? Proceedings of the International AAAI Conference on Web and Social Media, 409–418.
[4] Scikit-learn Documentation: https://scikit-learn.org/stable/ 2. NLTK Toolkit: https://www.nltk.org/ 3. Kaggle Datasets: Fake Reviews Detection Dataset 4. BERT for Text Classification — Devlin et al., Google AI (2018)
[5] Ray, S., & Chakraborty, M. (2020). Fake review detection using ensemble learning and text analytics. International Journal of Advanced Computer Science and Applications, 11(8), 110–118.
[6] Zhou, L., & Zafarani, R. (2020). A survey of fake news: Fundamental theories, detection methods, and opportunities. ACM Computing Surveys, 53(5), 1–40.
[7] Sharma, R., & Gupta, D. (2021). Detection of spam product reviews using machine learning and linguistic features. Journal of Big Data, 8(1), 1–15.
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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}
}