MindIntent: A Chatbot Using SVM for Identifying Mental Health Intervention Intentions

Mental health issues are increasingly acknowledged as vital, yet traditional support systems have limitations in availability, cost, and accessibility. Chatbots provide a scalable solution for real-time support, but their usefulness is dependent on correctly understanding user input. This work uses a Support Vector Machine (SVM) to improve chatbot performance in mental health applications by effectively dealing with high-dimensional data and discriminating between emotional states and intents. We created a chatbot by gathering and annotating text data, preprocessing it, and extracting features with tools like TF-IDF and word embeddings. The SVM model was trained on this data and implemented into the chatbot, allowing for real-time classification of user inputs and contextually relevant responses. Our results show that the SVM-powered chatbot excels at accurately categorizing users.