AI Powered Emotion Detection from Text for Mental Health Monitoring in Digital Communication

Digital communication is everywhere today, and it gives us a great chance to spot emotional signals that could point to mental health problems early on. This summary suggests creating an AI-based system that can automatically find and understand emotions in chats and emails. It would track how emotions change over time and match them with known psychological measures, giving useful insights to mental health professionals. The system would use advanced AI models (transformers) that are trained to detect different emotions at once. It would also protect people's privacy by hiding personal information, explain how the AI makes its decisions, and show real-time results on a dashboard to help with early support. This research hopes to improve emotion-detecting technology, help people who speak less common languages, and create ethical rules for using AI in mental health care. Keywords: emotion detection, mental‑health AI, transformer models, privacy, affective computing. Digital communication, Early detection, Chats and emails, psychological measures