Real-Time Sentiment Analysis for Monitoring Mental Health from Social Media Posts

Real-time sentiment analysis of social media content offers a powerful tool for tracking mental health trends and identifying at-risk individuals. This study employs natural language processing (NLP) and machine learning techniques to analyze user-generated content from platforms like Twitter, Reddit, and Instagram, assessing emotional tone and sentiment. A robust system is designed using deep learning models like transformers, BERT, and RoBERTa, achieving high accuracy in sentiment classification. This research proposes a scalable framework for real-time sentiment analysis, supporting early intervention in mental health monitoring while addressing ethical considerations.