Effective Analysis of Machine and Deep Learning Methods for Diagnosing Mental Health Using Social Media Conversations

The increasing incidence of mental health issues demands innovative diagnostic methods, especially within digital communication. Traditional assessments are challenged by the sheer volume of data and the nuanced language found on social media and other text-based platforms. This study seeks to apply machine learning (ML) to interpret these digital narratives and identify patterns that signal mental health conditions. We apply natural language processing (NLP) techniques to analyze sentiments and emotional cues across datasets from social media and other text-based communication. Using ML, deep learning, and transfer learning models such as bidirectional encoder representations (BERTs), robustly optimized BERT approach (RoBERTa), distilled BERT (DistilBERT), and generalized autoregressive pretraining for language understanding (XLNet), we assess their ability to detect early signs of mental health concerns. The results show that BERT, RoBERTa, and XLNet consistently achieve over 95% accuracy, highlighting their strong contextual understanding and effectiveness in this application. The significance of this research lies in its potential to revolutionize mental health diagnostics by providing a scalable, data-driven approach to early detection. By harnessing the power of advanced NLP models, this study offers a pathway to more timely and accurate identification of individuals in need of mental health support, thereby contributing to better outcomes in public health.