Abstract:
This project investigates how personalization and contextual understanding can enhance the effectiveness of NLP-based mental health chatbots for student support, focusing on the design and implementation of MindMate — an intelligent, privacy-focused conversational agent. The system integrates a fine-tuned RoBERTa model for multi-class emotion classification and Google Gemma-2B for context-aware response generation. By combining two publicly available datasets and applying label harmonization, class balancing, and stratified splitting, a robust seven-class emotion detection model was developed to recognize nuanced mental states commonly expressed by students.
MindMate’s backend is implemented using Flask, with MongoDB used to securely store encrypted chat histories and user interaction patterns while preserving confidentiality. The system incorporates personalized adaptation mechanisms, allowing responses to evolve based on the user’s emotional trends across sessions. A set of RESTful APIs handle user management, mental-state analysis, and conversational flow, enabling modularity, extensibility, and integration with future front-end applications.
The evaluation highlights MindMate’s ability to provide more empathetic, relevant, and stable conversations compared to baseline rule-based systems. The findings suggest that combining transformer-based emotion detection with lightweight generative models can significantly improve the quality of mental-health–oriented dialogue systems, particularly for students who often face stress, isolation, and academic pressure. Overall, the project demonstrates a practical and ethical approach to developing supportive digital mental-health tools that prioritize personalization, privacy, and user wellbeing.
