Enhancing TextGCN for depression detection on social media with emotion representation.

Background: Depression, also known as depressive disorder, is a pervasive mental health condition that affects individuals across diverse backgrounds and demographics. The detection of depression has emerged as a critical area of research in response to the growing global burden of mental health disorders. Objective: This study aims to augment the performance of TextGCN for depression detection by leveraging social media posts that have been enriched with emotional representation. Methods: We propose an enhanced TextGCN model that incorporate emotion representation learned from fine-tuned pre-trained language models, including MentalBERT, MentalRoBERTa, and RoBERTaDepressionDetection. Our approach involves integrating these models into TextGCN to capitalize on their emotional representation capabilities. Furthermore, unlike previous studies that discard emoticons and emojis as noise, we retain them as individual tokens during preprocessing to preserve potential affective cues. Results: The results demonstrate a significant improvement in performance achieved by the enhanced TextGCN models, when integrated with embeddings learned from MentalBERT, MentalRoBERTa, and RoBERTaDepressionDetection, compared to baseline models on five benchmark datasets. Conclusion: Our research highlights the potential of pre-trained models to enhance emotional representation in TextGCN, leading to improved detection accuracy, and can serve as a foundation for future research and applications in the mental health domain. In the forthcoming stages, we intend to refine our model by incorporating more balanced and targeted data sets, with the goal of exploring its potential applications in mental health.