Sentiment Analysis

With the growing popularity of online shopping, it has become essential to have reliable systems for evaluating products. This project creates a system that uses advanced natural language processing (NLP) techniques like Sentiment Analysis, Opinion Mining, and Aspect-Based Sentiment Analysis (ABSA) to provide accurate scores for products. It uses the Distil BERT model to analyze reviews and give ratings from 1 to 5 based on what customers write. ABSA helps the system break down feedback into specific areas, such as quality, price, and durability, offering more detailed insights. The system also detects emotions like happiness, anger, or sadness in reviews, giving a clearer picture of user opinions. It supports multiple languages, making it useful for analyzing reviews globally. Built with tools like Python, Beautiful Soup, NLTK, and Transformers, this system processes online reviews and converts them into easy-to-understand scores, helping buyers make better decisions.