DECODING EMOTIONS: HARNESSING REINFORCEMENT LEARNING FOR MENTAL ILLNESS DETECTION IN SOCIAL MEDIA

Аннотация

This study presents a pioneering approach to mental illness detection in social media through the application of reinforcement learning. With the exponential growth of online platforms, the intersection of mental health and social media usage becomes increasingly significant. Leveraging reinforcement learning algorithms, we aim to decode emotional patterns and identify potential signs of mental distress in user-generated content. The study combines natural language processing techniques, sentiment analysis, and reinforcement learning models to create a robust system for detecting mental health concerns. By harnessing the power of machine learning in the social media landscape, this research contributes to the development of proactive strategies for mental health support and intervention.