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This project aims to analyze opinions shared on Twitter by automatically determining whether a tweet expresses a positive, negative, or neutral sentiment. Tweets are collected using available APIs and cleaned to remove irrelevant elements such as links, emojis, hashtags, and common filler words. After preprocessing, text data is analyzed using Natural Language Processing techniques and trained machine learning models to identify sentiment patterns. The system helps in understanding public opinions, user attitudes, and trending reactions on social media. It can support organizations, researchers, and analysts in gaining insights into customer feedback, social issues, and online discussions in an efficient and scalable manner.This project presents an automated approach to understand public opinions by analyzing sentiment from Twitter data. It involves gathering tweets related to specific topics or keywords and refining the text through preprocessing steps such as tokenization, normalization, and noise removal. The refined data is then processed using Natural Language Processing methods and classification algorithms to determine the sentiment expressed in each tweet. The proposed system enables efficient analysis of large volumes of social media data, helping to identify user emotions, public reactions, and opinion trends. Such insights can be valuable for market analysis, social research, and decision-making processes where understanding public perception is important.This project focuses on analyzing public sentiment on Twitter to gain insights into opinions on specific topics or events. It integrates data collection, preprocessing, sentiment classification, and visualization into a unified system. By using NLP techniques and machine learning models, the project can accurately detect emotions expressed in tweets. The real-time analysis and interactive dashboards allow users to monitor trends efficiently. This approach demonstrates the practical application of AI and data analytics in understanding social media behavior.