Emotion-Aware AI Chatbots for Mental Health Support in Low-Resource Public Health Systems: A Case Study from Ghana

Mental health conditions are on the rise globally, yet many low-resource countries face systemic barriers such as stigma, underfunding, and a severe shortage of professionals to provide adequate care. This paper presents the design, implementation, and evaluation of an emotion-aware AI chatbot for mental health support within Ghana's public health context, aiming to bridge the mental health treatment gap. Leveraging deep learning for emotion detection and integrating a generative language model (GPT-3.5), the system delivers culturally relevant responses to users exhibiting symptoms of emotional distress. The study restructured the International Survey on Emotion Antecedents and Reactions (ISEAR) dataset into a 5-emotion model (Joy, Fear, Anger, Sadness, Neutral) to improve classification accuracy. A Convolutional Neural Network (CNN) emerged as the top-performing classifier (76.4% accuracy), outperforming LSTM, BiLSTM, and GRU models. This classifier was integrated with GPT-3.5 to enable context-aware, empathetic interactions. Field testing with 311 participants in Ghana revealed high satisfaction: 89% praised usability, 81% affirmed cultural relevance, and 78% reported emotional support. Notably, 66% felt encouraged to seek professional care, demonstrating the chatbot’s potential as a gateway to formal mental health services. The system’s anonymity and 24/7 accessibility addressed key barriers like stigma and resource limitations. The findings suggest that emotion-aware chatbots can complement mental health services in under-resourced settings and offer an innovative pathway for public health outreach. Future work will expand language options and crisis protocols. This research contributes a scalable, cost-effective model for global public health, emphasizing cultural adaptation and emotion-aware AI as critical tools in mental health innovation.