Artificial Intelligence based Framework for detecting Deceptive Online Reviews

This paper presented a system for detecting deceptive online reviews using a combination of natural language processing and machine learning techniques. The proposed approach integrates textual features, sentiment analysis, and reviewer behavior patterns to improve the accuracy of classification. Multiple models, including both traditional machine learning algorithms and deep learning methods, were evaluated to identify the most effective approach for distinguishing between genuine and fake reviews. The results indicate that combining different types of features leads to better performance compared to using a single feature type, while also maintaining a balance between accuracy and computational efficiency. The system is implemented through a web-based interface that provides predictions along with confidence scores and analytical insights, making it suitable for practical use. Although the current system performs effectively, its evaluation is limited to a controlled dataset, and future improvements can focus on handling larger datasets, adapting to evolving review patterns, and enhancing real-time detection capabilities

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