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The Rapid Growth Of Social Media Platforms, Particularly Twitter, Has Resulted In The Generation Of Vast Amounts Of User-generated Content That Reflects Public Opinions, Emotions, And Sentiments On Diverse Topics. Analyzing This Data Provides Valuable Insights For Businesses, Governments, And Researchers In Areas Such As Market Analysis, Policy-making, And Public Opinion Monitoring. This Project Focuses On Sentiment Analysis Of Twitter Data Using A Combination Of Machine Learning Approaches And Semantic Analysis Techniques. The Proposed System Involves Data Collection Through Twitter APIs, Preprocessing Of Textual Data Using Natural Language Processing (NLP) Methods, And The Application Of Supervised Learning Algorithms Such As Naïve Bayes, Support Vector Machines (SVM), And Logistic Regression For Sentiment Classification. Additionally, Semantic Analysis Techniques Are Integrated To Improve Context Understanding And Handle Challenges Such As Sarcasm, Ambiguity, And Slang, Which Are Common In Social Media Text. The Performance Of The Models Is Evaluated Using Metrics Like Accuracy, Precision, Recall, And F1-score To Determine Their Effectiveness. The Integration Of Machine Learning With Semantic Analysis Is Expected To Enhance Sentiment Detection Accuracy And Provide A Deeper Understanding Of Public Sentiment Trends On Twitter.