Vol.67, No.2, 2021, pp.1613-1627, doi:10.32604/cmc.2021.014265
COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries
  • Saleh Albahli1, Ahmad Algsham1, Shamsulhaq Aeraj1, Muath Alsaeed1, Muath Alrashed1, Hafiz Tayyab Rauf2,*, Muhammad Arif3, Mazin Abed Mohammed4
1 Department of Information Technology, College of Computer, Qassim University, Buraydah, Saudi Arabia
2 Department of Computer Science, Univesity of Gurjat, Pakistan
3 School of Computer Science, Guanzghou University, Guangzhou, 510006, China
4 College of Computer Science and Information technology, University of Anbar, 11, Ramadi, Anbar, Iraq
* Corresponding Author: Hafiz Tayyab Rauf. Email:
(This article belongs to this Special Issue: Intelligent techniques for energy efficient service management in Edge computing)
Received 10 September 2020; Accepted 19 November 2020; Issue published 05 February 2021
Social media has been the primary source of information from mainstream news agencies due to the large number of users posting their feedback. The COVID-19 outbreak did not only bring a virus with it but it also brought fear and uncertainty along with inaccurate and misinformation spread on social media platforms. This phenomenon caused a state of panic among people. Different studies were conducted to stop the spread of fake news to help people cope with the situation. In this paper, a semantic analysis of three levels (negative, neutral, and positive) is used to gauge the feelings of Gulf countries towards the pandemic and the lockdown, on basis of a Twitter dataset of 2 months, using Natural Language Processing (NLP) techniques. It has been observed that there are no mixed emotions during the pandemic as it started with a neutral reaction, then positive sentiments, and lastly, peaks of negative reactions. The results show that the feelings of the Gulf countries towards the pandemic depict approximately a 50.5% neutral, a 31.2% positive, and an 18.3% negative sentiment overall. The study can be useful for government authorities to learn the discrepancies between different populations from diverse areas to overcome the COVID-19 spread accordingly.
COVID-19; sentiment analysis; natural language processing; twitter; social data mining; sentiment polarity
Cite This Article
S. Albahli, A. Algsham, S. Aeraj, M. Alsaeed, M. Alrashed et al., "Covid-19 public sentiment insights: a text mining approach to the gulf countries," Computers, Materials & Continua, vol. 67, no.2, pp. 1613–1627, 2021.
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