Abstract

People are afraid about COVID-19 and are actively talking about it on social media platforms such as Twitter. People are showing their emotions openly in their tweets on Twitter. It's very important to perform sentiment analysis on these tweets for finding COVID-19's impact on people's lives. Natural language processing, textual processing, computational linguists, and biometrics are applied to perform sentiment analysis to identify and extract the emotions. In this work, sentiment analysis is carried out on a large Twitter dataset of English tweets. Ten emotional themes are investigated. Experimental results show that COVID-19 has spread fear/anxiety, gratitude, happiness and hope, and other mixed emotions among people for different reasons. Specifically, it is observed that positive news from top officials like Trump of chloroquine as cure to COVID-19 has suddenly lowered fear in sentiment, and happiness, gratitude, and hope started to rise. But, once FDA said, chloroquine is not effective cure, fear again started to rise.

Keywords

GratitudeSentiment analysisHappinessCoronavirus disease 2019 (COVID-19)Social mediaPandemicPsychologyAnxietySevere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)2019-20 coronavirus outbreakSocial psychologyPolitical scienceComputer scienceMedicineArtificial intelligenceLawPsychiatry

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Publication Info

Year
2021
Type
article
Volume
17
Issue
2
Pages
1-21
Citations
1616
Access
Closed

External Links

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23
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Cite This

Jalal S. Alowibdi, Abdulrahman A. Alshdadi, Ali Daud et al. (2021). Coronavirus Pandemic (COVID-19). International Journal on Semantic Web and Information Systems , 17 (2) , 1-21. https://doi.org/10.4018/ijswis.2021040101

Identifiers

DOI
10.4018/ijswis.2021040101

Data Quality

Data completeness: 77%