ThinkAI: A Natural Language Processing-based Intelligent framework for Mental Health

In the current digital era, emotional and mental health challenges are becoming very common. Therefore, it is essential to find new and effective ways to support emotional well-being. In this work, we propose ThinkAI helps individuals to better understand and manage their mental health. This platform offers a secure, explainable, and private space where users can express their thoughts and feelings through journaling. ThinkAI then leverages the help of natural language processing (NLP)-based algorithms to analyze these writings to identify emotional patterns. It tracks the changes in the mental well-being of individuals over time. It also offers visual insights that help users see how their emotional states have evolved during that time period, encouraging reflection, and self-awareness. The proposed system also generates alerts in case of adverse situations. ThinkAI comprises two main components: one focused on detecting early signs of depression, and the other on analyzing emotions. The system uses a combination of classical machine learning methods and modern transformer-based models to achieve accurate and reliable results. Both kinds of models were tested. For traditional models, the Support Vector Machine model reported the highest accuracy (0.920) and Receiver Operating Characteristic - Area Under the Curve (ROC-AUC) (0.97) for depression detection, while Naive Bayes had the best recall (0.947). For emotion analysis, Bidirectional Encoder Representations from Transformers (BERT) performed best with an accuracy of 0.945 and an F1-score of 0.9446, closely followed by robustly optimized BERT approach (RoBERTa) and Distilled Robustly Optimized BERT Approach (DistilRoBERTa). Furthermore, to provide a more comprehensive evaluation of the proposed models, we analyzed the training and validation loss across all models up to five epochs, in addition to reporting accuracy. The results highlight how combining classic algorithms with modern transformer-based deep learning models can create powerful tools for understanding emotional and mental health. Thus, ThinkAI offers a promising step toward real-time monitoring of mental health. This work contributes a technically validated and ethically grounded framework. It can be used for real-time monitoring of an individual's mental well-being, digital therapeutics, and large-scale psychological data analysis.