MENTAL HEALTH MONITORING THROUGH SOCIAL MEDIA SENTIMENT ANALYSIS

Abstract

There has also been a rise in psychological distress worldwide. An important medium that has asserted itself in this regard is social media. This has enabled people to share their feelings in a realistic manner. This large amount of user-generated content has opened the door for computational analysis. But diagnosing mental problems like depression, anxiety, and stress from their posts in a social media forum has remained a challenge. Traditional techniques in the lexis based approach and traditional machine learning algorithms have already been used for the purpose of sentiment classification; nonetheless, they are less capable of dealing with the complexities and patterns of online behavior in relation to emotions through valid contextual details. Recent innovations in the application of transformer models in the NLP for the purposes of emotion recognition and topic analysis support a better insight into the content and emotion-driven behavior patterns on platforms like Twitter, Reddit, and Facebook. The general assessment of various studies shows that these models have high predictive accuracy, and classification accuracy often exceeds 90 percent for identifying depressed or stress-related expressions. But again, most of these studies use proxy labels instead of professional labels, and issues like privacy, consent, and fairness have to be addressed carefully. In conclusion, AI-based sentiment analysis proves to be a potential real-time solution ready to be scaled up to monitor mental health, but also indicates the need to develop clinically oriented and ethical solutions.