IDIAP Submission@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text

Muskaan Singh, Petr Motlicek


Abstract
Depression is a common illness involving sadness and lack of interest in all day-to-day activities. It is important to detect depression at an early stage as it is treated at an early stage to avoid consequences. In this paper, we present our system submission of ARGUABLY for DepSign-LT-EDI@ACL-2022. We aim to detect the signs of depression of a person from their social media postings wherein people share their feelings and emotions. The proposed system is an ensembled voting model with fine-tuned BERT, RoBERTa, and XLNet. Given social media postings in English, the submitted system classify the signs of depression into three labels, namely “not depressed,” “moderately depressed,” and “severely depressed.” Our best model is ranked 3rd position with 0.54% accuracy . We make our codebase accessible here.
Anthology ID:
2022.ltedi-1.56
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:
362–368
Language:
URL:
https://aclanthology.org/2022.ltedi-1.56
DOI:
10.18653/v1/2022.ltedi-1.56
Bibkey:
Cite (ACL):
Muskaan Singh and Petr Motlicek. 2022. IDIAP Submission@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text. In Proceedings of the Second Workshop on Language Technology for Equality, Diversity and Inclusion, pages 362–368, Dublin, Ireland. Association for Computational Linguistics.
Cite (Informal):
IDIAP Submission@LT-EDI-ACL2022: Detecting Signs of Depression from Social Media Text (Singh & Motlicek, LTEDI 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.ltedi-1.56.pdf
Video:
 https://aclanthology.org/2022.ltedi-1.56.mp4