SSN_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text
Karun Anantharaman, Angel S, Rajalakshmi Sivanaiah, Saritha Madhavan, Sakaya Milton Rajendram
Abstract
DepSign-LT-EDI@ACL-2022 aims to ascer-tain the signs of depression of a person fromtheir messages and posts on social mediawherein people share their feelings and emo-tions. Given social media postings in English,the system should classify the signs of depres-sion into three labels namely “not depressed”,“moderately depressed”, and “severely de-pressed”. To achieve this objective, we haveadopted a fine-tuned BERT model. This solu-tion from team SSN_MLRG1 achieves 58.5%accuracy on the DepSign-LT-EDI@ACL-2022test set.- Anthology ID:
- 2022.ltedi-1.44
- Volume:
- Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion
- Month:
- May
- Year:
- 2022
- Address:
- Dublin, Ireland
- Editors:
- Bharathi Raja Chakravarthi, B Bharathi, John P McCrae, Manel Zarrouk, Kalika Bali, Paul Buitelaar
- Venue:
- LTEDI
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 296–300
- Language:
- URL:
- https://aclanthology.org/2022.ltedi-1.44
- DOI:
- 10.18653/v1/2022.ltedi-1.44
- Cite (ACL):
- Karun Anantharaman, Angel S, Rajalakshmi Sivanaiah, Saritha Madhavan, and Sakaya Milton Rajendram. 2022. SSN_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 296–300, Dublin, Ireland. Association for Computational Linguistics.
- Cite (Informal):
- SSN_MLRG1@LT-EDI-ACL2022: Multi-Class Classification using BERT models for Detecting Depression Signs from Social Media Text (Anantharaman et al., LTEDI 2022)
- PDF:
- https://preview.aclanthology.org/ingest-acl-2023-videos/2022.ltedi-1.44.pdf