Fake Product Review Detection using Machine Learning

Online product reviews play an important role in influencing customers’ purchasing decisions on e-commerce platforms. However, the increasing presence of fake or misleading reviews has significantly reduced the reliability of these systems and created challenges for both users and businesses in making trustworthy decisions. This paper presents a machine learning–based approach for identifying fake product reviews by analyzing textual patterns and review characteristics. The proposed system uses natural language processing techniques to preprocess review data and applies feature extraction methods such as TF-IDF to transform textual information into numerical form suitable for classification. Multiple classification algorithms are trained and evaluated to distinguish genuine reviews from deceptive ones effectively. The performance of the system is measured using evaluation metrics such as accuracy, precision, recall, and F1-score. Experimental results demonstrate that machine learning techniques can successfully detect suspicious reviews with satisfactory accuracy and reliability. The proposed approach can help e-commerce platforms improve the authenticity of the review, improve customer trust, and help users make better purchasing decisions. Future improvements may include the integration of deep learning models and real-time detection mechanisms for enhanced Conference performance.