Transformer-based classification of premise in tweets related to COVID-19

Vadim Porvatov, Natalia Semenova


Abstract
Automation of social network data assessment is one of the classic challenges of natural language processing. During the COVID-19 pandemic, mining people’s stances from their public messages become crucial regarding the understanding of attitude towards health orders. In this paper, authors propose the transformer-based predictive model allowing to effectively classify presence of stance and premise in the Twitter texts.
Anthology ID:
2022.smm4h-1.30
Volume:
Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task
Month:
October
Year:
2022
Address:
Gyeongju, Republic of Korea
Venue:
SMM4H
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
108–110
Language:
URL:
https://aclanthology.org/2022.smm4h-1.30
DOI:
Bibkey:
Cite (ACL):
Vadim Porvatov and Natalia Semenova. 2022. Transformer-based classification of premise in tweets related to COVID-19. In Proceedings of The Seventh Workshop on Social Media Mining for Health Applications, Workshop & Shared Task, pages 108–110, Gyeongju, Republic of Korea. Association for Computational Linguistics.
Cite (Informal):
Transformer-based classification of premise in tweets related to COVID-19 (Porvatov & Semenova, SMM4H 2022)
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PDF:
https://aclanthology.org/2022.smm4h-1.30.pdf