Emotion analysis and detection during COVID-19
Tiberiu Sosea, Chau Pham, Alexander Tekle, Cornelia Caragea, Junyi Jessy Li
Abstract
Understanding emotions that people express during large-scale crises helps inform policy makers and first responders about the emotional states of the population as well as provide emotional support to those who need such support. We present CovidEmo, a dataset of ~3,000 English tweets labeled with emotions and temporally distributed across 18 months. Our analyses reveal the emotional toll caused by COVID-19, and changes of the social narrative and associated emotions over time. Motivated by the time-sensitive nature of crises and the cost of large-scale annotation efforts, we examine how well large pre-trained language models generalize across domains and timeline in the task of perceived emotion prediction in the context of COVID-19. Our analyses suggest that cross-domain information transfers occur, yet there are still significant gaps. We propose semi-supervised learning as a way to bridge this gap, obtaining significantly better performance using unlabeled data from the target domain.- Anthology ID:
- 2022.lrec-1.750
- Volume:
- Proceedings of the Thirteenth Language Resources and Evaluation Conference
- Month:
- June
- Year:
- 2022
- Address:
- Marseille, France
- Venue:
- LREC
- SIG:
- Publisher:
- European Language Resources Association
- Note:
- Pages:
- 6938–6947
- Language:
- URL:
- https://aclanthology.org/2022.lrec-1.750
- DOI:
- Cite (ACL):
- Tiberiu Sosea, Chau Pham, Alexander Tekle, Cornelia Caragea, and Junyi Jessy Li. 2022. Emotion analysis and detection during COVID-19. In Proceedings of the Thirteenth Language Resources and Evaluation Conference, pages 6938–6947, Marseille, France. European Language Resources Association.
- Cite (Informal):
- Emotion analysis and detection during COVID-19 (Sosea et al., LREC 2022)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/2022.lrec-1.750.pdf
- Data
- COVIDEmo, GoEmotions, HurricaneEmo