DepBoost-TransNet: Boosted Transformer Network for Depression Classification

Depression presents a significant global mental health challenge affecting countless individuals across the globe. Early detection is of paramount importance, yet conventional methods, such as self-reporting and professional evaluations, possess inherent limitations and subjectivity. With the proliferation of social media and the abundance of user-generated content, novel approaches employing transformer-based models and text-mining techniques hold the potential to reveal signs of depression within social media text. This study delves into the efficacy of transformer-based models and natural language processing techniques in recognizing depressive symptoms within social media text. Social media serves as a distinct window into mental well-being, and this investigation explores the utilization of advanced transformers to tackle this issue. Results indicate that transformers trained on imbalanced datasets outperform those employing data augmentation techniques, like SMOTE, due to their proficiency in discerning intricate patterns and sensitivity to synthetic data. Furthermore, the ensemble model, which amalgamates predictions from multiple transformer models, surpasses individual models, underscoring the potential of harnessing the diverse strengths of these models for enhanced depression classification. Despite certain limitations and privacy considerations, leveraging social media data for early depression detection shows promise in improving the well-being of affected individuals through timely intervention and treatment. Subsequent research endeavors should prioritize hyperparameter optimization and the acquisition of more balanced datasets to further enhance the accuracy and dependability of depression diagnosis via the analysis of social media text.