Twitter Sentiment Analysis using Python (TweetX)

Twitter has become a significant platform for expressing opinions and emotions on a wide range of topics. This study presents a comprehensive analysis of sentiment expressed in Twitter data using natural language processing and machine learning techniques. The primary objective is to gauge public sentiment on various subjects, from politics to brand perception, by analysing tweets. We collected a vast dataset of tweets, pre-processed the text, and employed sentiment analysis algorithms to classify tweets as positive, negative, or neutral. The results provide valuable insights into public sentiment trends, allowing businesses, policymakers, and researchers to make informed decisions based on the collective voice of Twitter users. The study demonstrates the potential of sentiment analysis as a tool for understanding public sentiment and monitoring real-time public opinion Key Words: (NLTK), TextBlob