Mental Health and Stress Prediction Using NLP and Transformer-Based Techniques

The integration of Natural Language Processing (NLP) and machine learning technologies into the field of mental health and stress prediction represents a significant advancement in the early detection and intervention of mental health issues. This paper explores the application of various NLP and transformer-based techniques, such as TF-IDF, n-grams, BERT, RoBERTa, and ALBERT, to analyze textual data from diverse sources such as social media platforms towards mental health and stress prediction. The developed methodology involves comprehensive data preprocessing, innovative feature extraction, and the application of deep learning models to understand the refinement of language indicative of mental health states. By comparing the effectiveness of different models and approaches, this paper aims to establish a robust framework for accurately predicting mental health conditions, thereby contributing to the broader goal of improving mental health care through technology. The simulation performed on a real-life dataset obtained from Reddit reveals that RoBERTa-based techniques perform better than the other methods.