Public Perception of Myocarditis and Pericarditis Risk after Covid-19 Vaccination
- DOI
- 10.2991/978-94-6463-314-6_3How to use a DOI?
- Keywords
- Sentiment classification; Fine-tuned BERT Model; pretraining; Covid-19; vaccines; vaccination; myocarditis; pericarditis
- Abstract
Due to widespread development of COVID, there are health concerns across the world. The vaccinations are created as a result. Myocarditis and pericarditis are a couple of the adverse effects connected to COVID-19 immunizations, particularly the Pfizer-BioNTech and Moderna vaccines. As a result, it is necessary to examine people's sentiment. Social media is now a rich source of information where users may publish, comment, or tweet about their thoughts and experiences. In this study, we used a Fine-tuned BERT deep learning model and assess 2980 tweets from Twitter using tweepy to find people's perspectives on myocarditis and pericarditis following immunization. We discovered that negative tweets made up around 74.47% of the entire data while positive tweets made up 25.53% with Overall accuracy, F1-score, precision, and recall for the model were 0.911, 0.912, 0.926, 0.928 respectively.
- Copyright
- © 2023 The Author(s)
- Open Access
- Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.
Cite this article
TY - CONF AU - Anmolpreet Kaur AU - Kamaljit Kaur AU - Kiranbir Kaur PY - 2023 DA - 2023/12/21 TI - Public Perception of Myocarditis and Pericarditis Risk after Covid-19 Vaccination BT - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023) PB - Atlantis Press SP - 18 EP - 32 SN - 2589-4900 UR - https://doi.org/10.2991/978-94-6463-314-6_3 DO - 10.2991/978-94-6463-314-6_3 ID - Kaur2023 ER -