A Rigorous Comparative Study of Classical and Quantum Machine Learning for Intelligent Automated Fake Review Detection in Smart E-Commerce Systems

Author Details

The Extensive growth of e-commerce platforms has increased the prevalence of deceptive and fabricated reviews, posing challenges for automated trust assessment and intelligent online systems. Effective detection of such reviews requires models that can analyze textual patterns, linguistic cues, and sentiment characteristics with high reliability. This study presents a comparative evaluation of classical machine learning methods—Support Vector Machines (SVM) and k-Nearest Neighbors (KNN)—and their quantum counterparts, Quantum SVM (QSVM) and Quantum KNN (QKNN), for automated fake review detection. Unlike earlier works that rely on limited feature sets, this study incorporates comprehensive preprocessing and evaluates multiple performance metrics, including accuracy, precision, recall, and F1-score. The findings indicate that classical SVM consistently delivers strong and stable performance, while QSVM demonstrates modest but meaningful improvements in specific scenarios due to its ability to exploit higher-dimensional quantum feature spaces. The results highlight the potential of quantum-enhanced models for future intelligent automation systems, while also emphasizing current limitations related to computational overhead and scalability. This work contributes to the development of more robust AI-driven mechanisms for enhancing trust and reliability in smart e-commerce environments.

Keywords

Sentiment Analysis (SA), Fake Reviews Detection, Quantum Machine Learning (QML), Quantum Support Vector Machine (QSVM), Quantum k-nearest Neighbors (QKNN), Classic Machine Learning Algorithms, Text Classification, Performance Metrics, Quantum Circuits, Online Shopping Reviews.

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H. Vashistha and S. Kumari, “A rigorous comparative study of classical and quantum machine learning for intelligent automated fake review detection in smart e-commerce systems,” IPEM Journal of Computer Application & Research, vol. 10, pp. 65–76, Dec. 2025doi:.