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Original Research
Received: 12 Dec 2023, Accepted: 14 Dec 2023,
 


Sentiment Analysis on Arabic Public Opinions toward COVID-19 Vaccines Using Twitter Data

Najwa Alshahrani, Ghidaa Alnefaiy, Sara Abduljaleel, Tahani Alqurashi, Zahyah H Alharbi.

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    • Abstract
    Social media has emerged as a critical communication tool in contemporary society, bringing the world closer by making news and opinions more accessible through virtual platforms. Among these, Twitter stands out with over 350 million users worldwide. However, the highly unstructured nature of Twitter data poses significant challenges for analysis. Recently, the rich content of social media networks has spurred extensive research, particularly in light of the COVID-19 pandemic that has impacted global populations. Concurrent with the search for a vaccine, numerous studies have focused on analyzing public sentiment during the crisis, using data from various social media platforms. This study concentrates on people's opinions regarding COVID-19 vaccines, employing several machine learning models to analyze data from Twitter. It was discovered that the most accurate algorithm was the SGD Classifier, which utilized all three n-gram ranges, achieving an accuracy of 0.75.

    Key words: Arabic Tweets; COVID-19 Vaccines; Sentiment Analysis; Social Media;Twitter Data


    		 
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    How to Cite this Article
    Pubmed Style

    Alshahrani N, Alnefaiy G, Abduljaleel S, Alqurashi T, Alharbi ZH. Sentiment Analysis on Arabic Public Opinions toward COVID-19 Vaccines Using Twitter Data. Journal of Engineering and Applied Sciences. 2023; 10(2): 109-118. doi:10.5455/jeas.2023110108

    Web Style

    Alshahrani N, Alnefaiy G, Abduljaleel S, Alqurashi T, Alharbi ZH. Sentiment Analysis on Arabic Public Opinions toward COVID-19 Vaccines Using Twitter Data. https://jecasmu.org/?mno=180698 [Access: September 15, 2025]. doi:10.5455/jeas.2023110108

    AMA (American Medical Association) Style

    Alshahrani N, Alnefaiy G, Abduljaleel S, Alqurashi T, Alharbi ZH. Sentiment Analysis on Arabic Public Opinions toward COVID-19 Vaccines Using Twitter Data. Journal of Engineering and Applied Sciences. 2023; 10(2): 109-118. doi:10.5455/jeas.2023110108


    Vancouver/ICMJE Style

    Alshahrani N, Alnefaiy G, Abduljaleel S, Alqurashi T, Alharbi ZH. Sentiment Analysis on Arabic Public Opinions toward COVID-19 Vaccines Using Twitter Data. Journal of Engineering and Applied Sciences. (2023), [cited September 15, 2025]; 10(2): 109-118. doi:10.5455/jeas.2023110108


    Harvard Style

    Alshahrani, N., Alnefaiy, . G., Abduljaleel, . S., Alqurashi, . T. & Alharbi, . Z. H. (2023) Sentiment Analysis on Arabic Public Opinions toward COVID-19 Vaccines Using Twitter Data. Journal of Engineering and Applied Sciences, 10 (2), 109-118. doi:10.5455/jeas.2023110108


    Turabian Style

    Alshahrani, Najwa, Ghidaa Alnefaiy, Sara Abduljaleel, Tahani Alqurashi, and Zahyah H Alharbi. 2023. Sentiment Analysis on Arabic Public Opinions toward COVID-19 Vaccines Using Twitter Data. Journal of Engineering and Applied Sciences, 10 (2), 109-118. doi:10.5455/jeas.2023110108


    Chicago Style

    Alshahrani, Najwa, Ghidaa Alnefaiy, Sara Abduljaleel, Tahani Alqurashi, and Zahyah H Alharbi. "Sentiment Analysis on Arabic Public Opinions toward COVID-19 Vaccines Using Twitter Data." Journal of Engineering and Applied Sciences 10 (2023), 109-118. doi:10.5455/jeas.2023110108


    MLA (The Modern Language Association) Style

    Alshahrani, Najwa, Ghidaa Alnefaiy, Sara Abduljaleel, Tahani Alqurashi, and Zahyah H Alharbi. "Sentiment Analysis on Arabic Public Opinions toward COVID-19 Vaccines Using Twitter Data." Journal of Engineering and Applied Sciences 10.2 (2023), 109-118. Print. doi:10.5455/jeas.2023110108


    APA (American Psychological Association) Style

    Alshahrani, N., Alnefaiy, . G., Abduljaleel, . S., Alqurashi, . T. & Alharbi, . Z. H. (2023) Sentiment Analysis on Arabic Public Opinions toward COVID-19 Vaccines Using Twitter Data. Journal of Engineering and Applied Sciences, 10 (2), 109-118. doi:10.5455/jeas.2023110108


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    • ISSN: 1658-6638 (print)
    • ISSN: 1658-7073 (electronic)