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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: email

(This article belongs to the Special Issue: Intelligent techniques for energy efficient service management in Edge computing)

Computers, Materials & Continua 2021, 67(2), 1613-1627. https://doi.org/10.32604/cmc.2021.014265

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.

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APA Style
Albahli, S., Algsham, A., Aeraj, S., Alsaeed, M., Alrashed, M. et al. (2021). COVID-19 public sentiment insights: A text mining approach to the gulf countries. Computers, Materials & Continua, 67(2), 1613-1627. https://doi.org/10.32604/cmc.2021.014265
Vancouver Style
Albahli S, Algsham A, Aeraj S, Alsaeed M, Alrashed M, Rauf HT, et al. COVID-19 public sentiment insights: A text mining approach to the gulf countries. Comput Mater Contin. 2021;67(2):1613-1627 https://doi.org/10.32604/cmc.2021.014265
IEEE Style
S. Albahli et al., "COVID-19 Public Sentiment Insights: A Text Mining Approach to the Gulf Countries," Comput. Mater. Contin., vol. 67, no. 2, pp. 1613-1627. 2021. https://doi.org/10.32604/cmc.2021.014265

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cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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