NLP@NISER: Classification of COVID19 tweets containing symptoms

Deepak Kumar, Nalin Kumar, Subhankar Mishra


Abstract
In this paper, we describe our approaches for task six of Social Media Mining for Health Applications (SMM4H) shared task in 2021. The task is to classify twitter tweets containing COVID-19 symptoms in three classes (self-reports, non-personal reports & literature/news mentions). We implemented BERT and XLNet for this text classification task. Best result was achieved by XLNet approach, which is F1 score 0.94, precision 0.9448 and recall 0.94448. This is slightly better than the average score, i.e. F1 score 0.93, precision 0.93235 and recall 0.93235.
Anthology ID:
2021.smm4h-1.19
Volume:
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
Month:
June
Year:
2021
Address:
Mexico City, Mexico
Editors:
Arjun Magge, Ari Klein, Antonio Miranda-Escalada, Mohammed Ali Al-garadi, Ilseyar Alimova, Zulfat Miftahutdinov, Eulalia Farre-Maduell, Salvador Lima Lopez, Ivan Flores, Karen O'Connor, Davy Weissenbacher, Elena Tutubalina, Abeed Sarker, Juan M Banda, Martin Krallinger, Graciela Gonzalez-Hernandez
Venue:
SMM4H
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
102–104
Language:
URL:
https://aclanthology.org/2021.smm4h-1.19
DOI:
10.18653/v1/2021.smm4h-1.19
Bibkey:
Cite (ACL):
Deepak Kumar, Nalin Kumar, and Subhankar Mishra. 2021. NLP@NISER: Classification of COVID19 tweets containing symptoms. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 102–104, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
NLP@NISER: Classification of COVID19 tweets containing symptoms (Kumar et al., SMM4H 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.smm4h-1.19.pdf