Dictionary Based Global Twitter Sentiment Analysis of Coronavirus (COVID-19) Effects and Response

Elphas Okango () and Henry Mwambi
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Elphas Okango: University of KwaZulu-Natal
Henry Mwambi: University of KwaZulu-Natal

Annals of Data Science, 2022, vol. 9, issue 1, No 10, 175-186

Abstract: Abstract In December 2019, a new pandemic called the coronavirus began ravaging the world. By May 2020, the pandemic had caused great loss of lives and disrupted the way of lives in more ways than one. The nature of the disease saw several strategies to curb its spread rolled out. These strategies included closing of businesses and borders, restriction of movements and working from home, mask mandate among others. With these measures and the effects, many individuals have taken to the social media to express their frustrations, opinions and how the pandemic is affecting them. This study employs dictionary based method for sentiment polarization from tweets related to coronavirus posted on Twitter. We also examine the co-occurrence of words to gain insights on the aspects affecting the masses. The results showed that mental health issues, lack of supplies were some of the direct effects of the pandemic. It was also clear that the COVID-19 prevention guidelines were well understood by those who tweeted. The results from this study may help governments combat the consequences of COVID-19 like mental health issues, lack of supplies e.g. food and also gauge the effectiveness or the reach of their guidelines.

Keywords: Coronavirus; Lexicon; Sentiment; Word co-occurrence; Valance shifters (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1007/s40745-021-00358-5

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