• KSII Transactions on Internet and Information Systems
    Monthly Online Journal (eISSN: 1976-7277)

Sentiment Analysis for COVID-19 Vaccine Popularity

Vol. 17, No. 5, May 31, 2023
10.3837/tiis.2023.05.004, Download Paper (Free):


Social media is used for various purposes including entertainment, communication, information search, and voicing their thoughts and concerns about a service, product, or issue. The social media data can be used for information mining and getting insights from it. The World Health Organization has listed COVID-19 as a global epidemic since 2020. People from every aspect of life as well as the entire health system have been severely impacted by this pandemic. Even now, after almost three years of the pandemic declaration, the fear caused by the COVID-19 virus leading to higher depression, stress, and anxiety levels has not been fully overcome. This has also triggered numerous kinds of discussions covering various aspects of the pandemic on the social media platforms. Among these aspects is the part focused on vaccines developed by different countries, their features and the advantages and disadvantages associated with each vaccine. Social media users often share their thoughts about vaccinations and vaccines. This data can be used to determine the popularity levels of vaccines, which can provide the producers with some insight for future decision making about their product. In this article, we used Twitter data for the vaccine popularity detection. We gathered data by scraping tweets about various vaccines from different countries. After that, various machine learning and deep learning models, i.e., naive bayes, decision tree, support vector machines, k-nearest neighbor, and deep neural network are used for sentiment analysis to determine the popularity of each vaccine. The results of experiments show that the proposed deep neural network model outperforms the other models by achieving 97.87% accuracy.


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Cite this article

[IEEE Style]
M. Saeed, N. Ahmed, A. Mehmood, M. Aftab, R. Amin, S. Kamal, "Sentiment Analysis for COVID-19 Vaccine Popularity," KSII Transactions on Internet and Information Systems, vol. 17, no. 5, pp. 1377-1395, 2023. DOI: 10.3837/tiis.2023.05.004.

[ACM Style]
Muhammad Saeed, Naeem Ahmed, Abid Mehmood, Muhammad Aftab, Rashid Amin, and Shahid Kamal. 2023. Sentiment Analysis for COVID-19 Vaccine Popularity. KSII Transactions on Internet and Information Systems, 17, 5, (2023), 1377-1395. DOI: 10.3837/tiis.2023.05.004.

[BibTeX Style]
@article{tiis:45183, title="Sentiment Analysis for COVID-19 Vaccine Popularity", author="Muhammad Saeed and Naeem Ahmed and Abid Mehmood and Muhammad Aftab and Rashid Amin and Shahid Kamal and ", journal="KSII Transactions on Internet and Information Systems", DOI={10.3837/tiis.2023.05.004}, volume={17}, number={5}, year="2023", month={May}, pages={1377-1395}}