Flipkart Sentiment Analysis

In the era of digital commerce, platforms like Flipkart have revolutionized the shopping experience, offering users a wide variety of products along with access to customer reviews and ratings. These reviews serve as a crucial reference for potential buyers in assessing product quality and reliability. However, manually interpreting thousands of reviews is inefficient and prone to human error. This project presents an advanced sentiment analysis system powered by Machine Learning (ML) and Natural Language Processing (NLP) to automatically evaluate Flipkart product reviews. The proposed system effectively preprocesses review data, extracts meaningful features, and classifies sentiments as positive, negative, or neutral using traditional ML algorithms like SVM, Naïve Bayes, and Random Forest. Additionally, the system integrates a fake review detection module using anomaly detection techniques based on user behavior and review patterns. A real-time dashboard is also developed to provide users and sellers with accurate, actionable insights into product sentiment. The solution not only enhances the trustworthiness of reviews but also empowers businesses to understand customer preferences, improve products, and detect fraudulent feedback.