Fake Review Detection in E-Commerce Using Machine Learning and NLP Technique

The credibility of online product reviews has a direct impact on consumer trust and purchasing behavior. However, the proliferation of fake reviews—often crafted to manipulate public opinion—poses a serious threat to the reliability of digital marketplaces. This project presents a robust machine learning framework to detect fake reviews by analyzing both textual content and reviewer behavior. Using Natural Language Processing (NLP) techniques, the system extracts features such as sentiment polarity, n-gram frequency, and temporal review patterns. Several machine learning algorithms, including K-Nearest Neighbors (KNN), Logistic Regression (LR), and Support Vector Machines (SVM), are trained and evaluated using a real-world dataset obtained from Kaggle. Experimental results reveal that the SVM classifier outperforms other models with a detection accuracy of 96% and an F1-score of 92.4%. The results demonstrate the effectiveness of combining linguistic features with metadata in enhancing fake review classification. This work contributes toward building more transparent and trustworthy online platforms through automated review verification.