Authors: Ms. Gude Kalyani
Abstract: Mental health challenges are rising worldwide, making early detection and monitoring increasingly important. With millions of people actively sharing thoughts and emotions on platforms like Facebook, Twitter, and Reddit, social media has become a valuable resource for understanding mental well-being. Earlier studies relied mainly on traditional machine learning (ML) techniques such as logistic regression, support vector machines, random forests, and ensemble models. These methods achieved only moderate results and often struggled with the complexity of natural language and diverse forms of data, limiting their effectiveness in real-world use. This work introduces a Mental Health Diagnostics framework that combines both social media data and personal details—such as age, family history, medical leave, and workplace challenges—to predict mental health conditions. The system applies a wide range of ML and deep learning (DL) approaches, with particular focus on a hybrid model that blends Bidirectional Long Short-Term Memory (BLSTM) with Convolutional Neural Networks (CNN). This design captures both sequential patterns and key contextual features, offering stronger predictive performance. Together with advanced models like RoBERTa and other ensemble methods, the proposed system achieves 99.6% accuracy. The findings demonstrate how integrating structured inputs with social media insights can create a reliable, scalable, and practical tool for mental health prediction, supporting early interventions and improved digital healthcare solutions.
DOI: https://doi.org/10.5281/zenodo.17062374
