MoodMap: Integrating NLP and AI for Psychological Well-Being and Sentiment Detection

Mental health issues such as stress, anxiety, and depression are becoming more prevalent, yet many people struggle to access timely support. This paper presents an innovative system that uses advanced Natural Language Processing (NLP) techniques for emotion detection and mental health assessment. Using pre-trained models like DistilBERT, the system analyzes text inputs to identify emotions such as joy, fear, sadness, and anger, along with corresponding confidence scores. In addition to emotion detection, a structured mental health quiz, based on validated psychological frameworks, evaluates key indicators such as stress, anxiety, and mood swings. Generate personalized recommendations, including stress management strategies, self-care routines, and advice on how to seek professional help. This paper is a significant step toward AI-driven mental health technologies, offering a scalable, user-friendly, and privacy-conscious solution for early detection and intervention. Combining NLP-based emotion detection with structured mental health assessment is a contribution to the growing field of intelligent mental health support systems, bridging the gap between technology and well-being.