DeepLearningBrasil@LT-EDI-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text

• URL: http://arxiv.org/abs/2311.05047v1
• Date: Wed, 8 Nov 2023 22:42:31 GMT
• Title: DeepLearningBrasil@LT-EDI-2023: Exploring Deep Learning Techniques for Detecting Depression in Social Media Text
• Authors: Eduardo Garcia, Juliana Gomes, Adalberto Barbosa J\'unior, Cardeque Borges, N\'adia da Silva
• Abstract summary: We achieve a 47.0% Macro F1-Score and a notable 2.4% advantage in the shared task DepSign-LT-EDI@RANLP-2023. The task was to classify social media texts into three distinct levels of depression - "not depressed," "moderately depressed," and "severely depressed"
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this paper, we delineate the strategy employed by our team, DeepLearningBrasil, which secured us the first place in the shared task DepSign-LT-EDI@RANLP-2023, achieving a 47.0% Macro F1-Score and a notable 2.4% advantage. The task was to classify social media texts into three distinct levels of depression - "not depressed," "moderately depressed," and "severely depressed." Leveraging the power of the RoBERTa and DeBERTa models, we further pre-trained them on a collected Reddit dataset, specifically curated from mental health-related Reddit's communities (Subreddits), leading to an enhanced understanding of nuanced mental health discourse. To address lengthy textual data, we used truncation techniques that retained the essence of the content by focusing on its beginnings and endings. Our model was robust against unbalanced data by incorporating sample weights into the loss. Cross-validation and ensemble techniques were then employed to combine our k-fold trained models, delivering an optimal solution. The accompanying code is made available for transparency and further development.

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