Sentiment Analysis Using Web Scrapped Data

This study presents a web-scraped data-driven Sentiment Analysis System designed to extract and evaluate public opinions from diverse online platforms. Built with robust data collection techniques, the system gathers real-time textual data, which is then processed using Natural Language Processing (NLP) models to determine sentiment polarity—positive, negative, or neutral. The project emphasizes the structuring of large datasets, efficient data cleaning methods, and the application of machine learning algorithms to ensure accurate sentiment classification. Furthermore, it explores challenges in handling noisy data, managing multilingual content, and optimizing model accuracy. This analysis provides valuable insights for businesses, social researchers, and policymakers to understand public perception and trends.