Abstract
Social media, such as Twitter, is a source of exchanging information and opinion on global issues such as COVID-19 pandemic. In this study, we work with a database of around 1.2 million tweets collected across five weeks of April–May 2021 to draw conclusions about public sentiments towards the vaccination outlook when vaccinations become widely available to the population during the COVID-19 pandemic. We deploy natural language processing and sentiment analysis techniques to reveal insights about COVID-19 vaccination awareness among the public. Our results show that people have positive sentiments towards taking COVID-19 vaccines instead of some adverse effects of some of the vaccines. We also analyze people’s attitude towards the safety measures of COVID-19 after receiving the vaccines. Again, the positive sentiment is higher than that of negative in terms of maintaining safety measures against COVID-19 among the vaccinated population. We also project that around 62.44% and 48% of the US population will get at least one dose of vaccine and be fully vaccinated, respectively, by the end of July 2021 according to our forecast model. This study will help to understand public reaction and aid the policymakers to project the vaccination campaign as well as health and safety measures in the ongoing global health crisis.
Original language | English |
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Article number | 6128 |
Journal | Applied Sciences (Switzerland) |
Volume | 11 |
Issue number | 13 |
DOIs | |
State | Published - Jul 1 2021 |
Externally published | Yes |
Funding
Funding: This work has been partially supported by Louisiana Board of Regents RCS Grant LEQSF (2017-20)-RDA-25.
Funders | Funder number |
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Louisiana Board of Regents | LEQSF (2017-20)-RDA-25 |
Keywords
- COVID-19
- Natural language processing
- Public sentiment
- Sentiment analysis
- Social network
- Time series forecasting
- Twitter data
- Vaccination