Tweetsense: Emotion Detection From Twitter Data Using Natural Language Processing

Abstract

This Research Investigates The Application Of Natural Language Processing (NLP) Models For Sentiment Analysis Of Twitter Data, A Domain That Has Gained Immense Importance In The Era Of Digital Communication.[1] Twitter, As A Microblogging Platform, Generates Millions Of Short Text Messages Daily, Reflecting Public Opinion On Diverse Subjects Ranging From Politics And Business To Entertainment And Social Issues. The Brevity And Informality Of Tweets, Combined With The Use Of Slang, Abbreviations, Emojis, And Multilingual Expressions, Make Sentiment Classification A Challenging Task.[2]The Proposed Framework Integrates Preprocessing Techniques, Feature Extraction Methods, And Advanced Machine Learning And Deep Learning Models To Classify Tweets Into Positive, Negative, Or Neutral Sentiments.[3] Transformer-based Architectures Such As BERT And RoBERTa Are Employed To Capture Contextual Meaning, Thereby Improving Classification Accuracy.[4] The System Is Designed To Provide Real-time Sentiment Monitoring, Enabling Stakeholders To Track Public Opinion Trends And Make Informed Decisions.[5][6] This Study Contributes To The Growing Field Of Social Media Analytics By Offering A Scalable, Efficient, And Accurate Solution For Sentiment Analysis.[7]-

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