AI-Based Fake Product Review Detection Using NLP and Machine Learning

The growing issue of fake product reviews presents a significant challenge for e-commerce platforms, leading to distorted customer perceptions and potentially harmful decisions. This project proposes an automated system for detecting fake product reviews using machine learning techniques and Natural Language Processing (NLP). The system utilizes algorithms like Random Forest and XGBoost to classify reviews into three categories: Positive (Favorable), Neutral (Moderate), and Negative (Critical). By analyzing text-based features such as sentiment, word frequency, and context, the model identifies the authenticity of reviews. The best-performing model is deployed in a web-based application, enabling users to upload reviews and receive feedback on their legitimacy. This solution offers a reliable and efficient method to enhance trust and transparency in online review systems, providing consumers and businesses with an effective tool for identifying deceptive content. Additionally, the system continuously improves through user feedback, ensuring higher accuracy over time. The proposed approach can be adapted to various platforms, contributing to the broader effort to combat online fraud and maintain the integrity of user-generated content.

Keywords:
Fake Product Review Detection, Natural Language Processing (NLP), Machine Learning, Random Forest, XGBoost, Sentiment Analysis, Text Classification, Review Categorization, Fake Review Identification, Web-Based Deployment.