NLP-for-Mental-Health-MIT-URTC-2024-

As social media becomes more prevalent in the contemporary age, it becomes increasingly reflective of its users’ thoughts and emotions. These platforms serve as windows to gauge their users’ mental health and make accurate assessments and diagnoses to treat these disorders. This study explores a novel RoBERTa deep learning model to accurately classify various mental health abnormalities through contextual embeddings to capture the nuanced language patterns indicative of conditions like depression, anxiety, and bipolar disorder. The model developed in this paper was trained on Reddit posts and evaluated using metrics such as accuracy, precision, recall, and F-1 Score. This novel RoBERTa model was also benchmarked against traditional methods like logistic regression with TF-IDF and a CNN-LSTM hybrid. This study also faced significant limitations such as computation constraints, limiting the training to only three epochs, paving the way for future research to address these limitations.