Classification of COVID19 tweets using Machine Learning Approaches

Anupam Mondal, Sainik Mahata, Monalisa Dey, Dipankar Das


Abstract
The reported work is a description of our participation in the “Classification of COVID19 tweets containing symptoms” shared task, organized by the “Social Media Mining for Health Applications (SMM4H)” workshop. The literature describes two machine learning approaches that were used to build a three class classification system, that categorizes tweets related to COVID19, into three classes, viz., self-reports, non-personal reports, and literature/news mentions. The steps for pre-processing tweets, feature extraction, and the development of the machine learning models, are described extensively in the documentation. Both the developed learning models, when evaluated by the organizers, garnered F1 scores of 0.93 and 0.92 respectively.
Anthology ID:
2021.smm4h-1.29
Volume:
Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task
Month:
June
Year:
2021
Address:
Mexico City, Mexico
Venues:
NAACL | SMM4H
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
135–137
Language:
URL:
https://aclanthology.org/2021.smm4h-1.29
DOI:
10.18653/v1/2021.smm4h-1.29
Bibkey:
Cite (ACL):
Anupam Mondal, Sainik Mahata, Monalisa Dey, and Dipankar Das. 2021. Classification of COVID19 tweets using Machine Learning Approaches. In Proceedings of the Sixth Social Media Mining for Health (#SMM4H) Workshop and Shared Task, pages 135–137, Mexico City, Mexico. Association for Computational Linguistics.
Cite (Informal):
Classification of COVID19 tweets using Machine Learning Approaches (Mondal et al., SMM4H 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.smm4h-1.29.pdf