Diagnosis of Mental Health States Using Hybrid Emotional Analysis Techniques

Authors- Madhan Suriya k, Associate professor Dr. C. Meenakshi

Abstract- Mental health disorders are an emergent priority issue that impacts the emotional, physical, and social well-being of people across the globe. Detection of the disorders at an early stage is necessary to prevent severe impacts and improve the mental health. The project addresses the issue of long-duration and subjective mental health screening by proposing an innovative solution that employs objective, technology-based approaches. The main goal is to create an accessible, real-time, and scalable system for end-to-end mental health assessment. The proposed system, the Hybrid Mental Health Analysis System, uses Natural Language Processing (NLP) for text-based emotion recognition and Facial Emotion Recognition (FER) for facial emotion detection. The novelty lies in the multi-modal fusion of facial and text-based information to create a uniform emotional profile and subsequently enable accurate classification of mental health status into positive, neutral, and negative. User-specific recommendations and helpline support further enhance user engagement and promote emotional well- being. The proposed system has potential applications in healthcare, education, corporate wellness, and social media industries.

DOI: /10.61463/ijset.vol.13.issue3.122