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


  • 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


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.


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


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How to Cite

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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