Abstract
Depression, a widespread mental health disorder, affects a significant portion of the global population. Timely identification and intervention play a crucial role in ensuring effective treatment and support. Therefore, this research paper proposes a fine-tuned RoBERTa-based model for identifying depression in social media posts. In addition to the proposed model, Sentence-BERT is employed to encode social media posts into vector representations. These encoded vectors are then utilized in eight different popular classical machine learning models. The proposed fine-tuned RoBERTa model achieved a best macro F1-score of 0.55 for the development dataset and a comparable score of 0.41 for the testing dataset. Additionally, combining Sentence-BERT with Naive Bayes (S-BERT + NB) outperformed the fine-tuned RoBERTa model, achieving a slightly higher macro F1-score of 0.42. This demonstrates the effectiveness of the approach in detecting depression from social media posts.- Anthology ID:
- 2023.ltedi-1.13
- Volume:
- Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion
- Month:
- September
- Year:
- 2023
- Address:
- Varna, Bulgaria
- Editors:
- Bharathi R. Chakravarthi, B. Bharathi, Joephine Griffith, Kalika Bali, Paul Buitelaar
- Venues:
- LTEDI | WS
- SIG:
- Publisher:
- INCOMA Ltd., Shoumen, Bulgaria
- Note:
- Pages:
- 89–96
- Language:
- URL:
- https://aclanthology.org/2023.ltedi-1.13
- DOI:
- Cite (ACL):
- Jyoti Kumari and Abhinav Kumar. 2023. JA-NLP@LT-EDI-2023: Empowering Mental Health Assessment: A RoBERTa-Based Approach for Depression Detection. In Proceedings of the Third Workshop on Language Technology for Equality, Diversity and Inclusion, pages 89–96, Varna, Bulgaria. INCOMA Ltd., Shoumen, Bulgaria.
- Cite (Informal):
- JA-NLP@LT-EDI-2023: Empowering Mental Health Assessment: A RoBERTa-Based Approach for Depression Detection (Kumari & Kumar, LTEDI-WS 2023)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2023.ltedi-1.13.pdf