Vol.67, No.2, 2021, pp.1613-1627, doi:10.32604/cmc.2021.014265
OPEN ACCESS
ARTICLE
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
Abstract
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.
Keywords
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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