Context-Based Psychosis Emotion Recognition System

In the evolving landscape of mental health diagnostics, the identification and understanding of emotional states, particularly in individuals with psychosis, stand as paramount challenges. This research introduces a novel approach termed “Context-Based Psychosis Emotion Recognition,” aimed at leveraging advanced language models like RoBERTa for refined emotion detection in individuals exhibiting signs of psychosis. By integrating the deep contextual understanding capabilities of RoBERTa with context-based features specific to psychosis, the system achieves heightened accuracy in recognizing and categorizing nuanced emotional states. The methodology incorporates a multi-layered analysis, first extracting raw textual data from patient interactions and then subjecting it to RoBERTa's sophisticated language processing. Subsequent layers of the model are fine-tuned using specialized datasets focusing on psychosis-related emotions. Preliminary results indicate a significant enhancement in emotion recognition accuracy, providing healthcare professionals with invaluable insights into the emotional complexities associated with psychosis. This proj ect not only underscores the potential of transformer-based models in mental health applications but also emphasizes the ethical and privacy considerations essential for deploying such technologies in clinical settings. As a pioneering effort, this research paves the way for advanced diagnostic tools tailored to the intricate emotional landscapes of individuals with psychosis.