Fake Product Review Detection and Sentiment Analysis using Machine Learning

Author(s)	Ms. Priti Ashok Bankar, Prof. Manisha Patil
Country	India
Abstract	This study focuses on detecting fake product reviews using supervised machine learning techniques. The framework combines data collection, preprocessing, feature extraction, and feature selection methods to improve review classification. Two machine learning models, Artificial Neural Network (ANN) and Random Forest, were evaluated using accuracy, precision, recall, and F1-score metrics. The ANN model achieved 95.88% accuracy with an F1-score of 98.06%, while the Random Forest model achieved 97.94% accuracy with 100% precision, recall, and F1-score. The results demonstrate that Random Forest outperforms ANN in detecting fraudulent reviews, providing a reliable and effective solution. Future research may incorporate deep learning and real-time analysis to further enhance fake review detection systems.
Keywords	Fake reviews, Sentiment analysis, Machine Learning, Deep Learning, Opinion mining, Natural Language Processing (NLP)
Field	Engineering
Published In	Volume 8, Issue 3, May-June 2026
Published On	2026-05-13

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International Journal For Multidisciplinary Research