Emotion-aware psychological first aid: Integrating BERT-based emotional distress detection with Psychological First Aid-Generative Pre-Trained Transformer chatbot for mental health support

Abstract

Mental health disorders have a global prevalence of 25%, according to the WHO, and this is exacerbated by factors such as stigma, geographical location, and a worldwide shortage of practitioners. Mental health chatbots have been developed to address these barriers, but these systems lack key features such as emotion recognition, personalisation, multilingual support, and ethical appropriateness. This paper introduces an innovative mental health support system that integrates BERT-based emotional distress detection with a psychological first aid (PFA)-generative pre-trained transformer (PFA-GPT) model, providing an emotion-aware PFA chatbot. The methodology leverages deep learning models, utilising bidirectional encoder representations from transformers (BERT) for emotional distress detection and fine-tuning GPT-3.5 on therapy transcripts for PFA chatbot development. The findings demonstrate BERT's superior accuracy (93%) for emotional distress detection compared to bidirectional long short-term memory. The multilingual PFA chatbot developed using the PFA-GPT model demonstrated superior BERT scores (exceeding 83%) and proficiently provided ethical PFA. A proof of concept has been developed to illustrate the integration of the emotional distress detection model with the novel generative conversational agent for PFA. This integrated approach holds significant potential in overcoming existing barriers to mental health support and has the potential to transform mental health support, offering timely and accessible care through AI-powered psychological interventions.