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
- Editors:
- Graciela Gonzalez-Hernandez, Davy Weissenbacher
- Venue:
- SMM4H
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 108–110
- Language:
- URL:
- https://aclanthology.org/2022.smm4h-1.30
- DOI:
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2022.smm4h-1.30.pdf