Revolutionizing Mental Health Counseling with Serenity: An Emotion-Detecting Chatbot

Mental health counseling is a significant challenge in contemporary society, primarily due to issues such as cost, stigma, fear, and limited availability. Emotions play a crucial role in conveying information in this context, making emotion detection essential for a deeper understanding of an individual’s mental well-being. Utilizing generative machine learning models in mental health counseling could potentially lower barriers to access and improve outcomes. This paper proposes the development of a deep learning-based emotion-detecting chatbot named Serenity. The approach involves combining a pre-trained deep neural model, RoBERTa, with a multi-resolution adversarial model, EmpDG, to enhance the accuracy of detected emotions and generate more empathetic responses. RoBERTa has been trained on a dataset of thousands of tweets from Twitter. Additionally, an interactive adversarial learning framework is introduced to leverage user feedback and assess the emotional perceptivity of generated responses in dialogues. The study aims to demonstrate that a machine learning-based mental health chatbot like Serenity has the potential to serve as an effective complement to traditional human counselors.