Sentiment Analysis of Public Tweets Towards the Emergence of SARS-CoV-2 Omicron Variant: A Social Media Analytics Framework

Authors

  • M. Mahyoob Department of Languages and Translation, Faculty of Science & Arts at Al-Ola, Taibah University, Saudi Arabia
  • J. Algaraady Department of English, Taiz University, Yemen
  • M. Alrahiali Department of Languages and Translation, Faculty of Arts & Humanities, Taibah University, Saudi Arabia
  • A. Alblwi Department of Computer Science, Applied College, Taibah University, Saudi Arabia
Volume: 12 | Issue: 3 | Pages: 8525-8531 | June 2022 | https://doi.org/10.48084/etasr.4865

Received: 22 February 2022 | Revised: 16 March 2022 | Accepted: 25 March 2022 | Online: 14 April 2022


Corresponding author: M. Mahyoob

Abstract

While different variants of COVID-19 dramatically affected the lives of millions of people across the globe, a new version of COVID-19, "SARS-CoV-2 Omicron," emerged. This paper analyzes the public attitude and sentiment towards the emergence of the SARS-CoV-2 Omicron variant on Twitter. The proposed approach relies on the text analytics of Twitter data considering tweets, retweets, and hashtags' main themes, the pandemic restriction, the efficacy of covid-19 vaccines, transmissible variants, and the surge of infection. A total of 18,737 tweets were pulled via Twitter Application Programming Interface (API) from December 3, 2021, to December 26, 2021, using the SentiStrength software that employs a lexicon of sentiment terms and a set of linguistic rules. The analysis was conducted to distinguish and codify subjective content and estimate the strength of positive and negative sentiment with an average of 95% confidence intervals based upon emotion strength scales of 1-5. It is found that negativity was dominated after the outbreak of Omicron and scored 31.01% for weak, 16.32% for moderate, 5.36% for strong, and 0.35% for very strong sentiment strength. In contrast, positivity decreased gradually and scored 16.48% for weak, 11.19% for moderate, 0.80% for strong, and 0.04% for very strong sentiment strength. Identifying the public emotional status would help the concerned authorities to provide appropriate strategies and communications to relieve public worries about pandemics.

Keywords:

sentiment analysis, social media, SARS-CoV-2 Omicron, Twitter, text analytics, data mining

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[1]
M. Mahyoob, J. Algaraady, M. Alrahiali, and A. Alblwi, “Sentiment Analysis of Public Tweets Towards the Emergence of SARS-CoV-2 Omicron Variant: A Social Media Analytics Framework”, Eng. Technol. Appl. Sci. Res., vol. 12, no. 3, pp. 8525–8531, Jun. 2022.

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