Sentiment Analysis for Detecting Early Signs of Depression in Social Media Posts : Emerging Technologies for Health Care Workshop organized by AIIMS Delhi, MeitY, and CDAC scheduled on October 22, 2024.

Depression remains a critical public health challenge in India, with social stigma often preventing individuals from seeking help. Social media, where individuals frequently express emotions, offers an alternative source for detecting early signs of depression. This study explores the potential of Natural Language Processing (NLP) and Machine Learning techniques to analyze Twitter posts for identifying depressive symptoms. By examining user profiles, linguistic patterns, emoticon usage, posting times, and topical content, we identified key features that may indicate depressive tendencies. Experimental results using an XGBoost classifier demonstrate the efficacy of these features, particularly linguistic and topical indicators, in detecting depression-related language. This research highlights the potential of social media analysis for early intervention while noting the need for future studies incorporating additional temporal and semantic features to enhance prediction accuracy.