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Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia

Vaccination is the primary preventive measure against the COVID-19 infection, and an additional vaccine dosage is crucial to increase the immunity level of the community. However, public bias, as reflected on social media, may have a significant impact on the vaccination program. We aim to investiga...

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Main Authors: Ong, Song Quan, Maisarah Mohamed Pauzi, Keng, Hoon Gan
Format: Article
Language:English
English
Published: Multidisciplinary Digital Publishing Institute 2022
Subjects:
RA638 Immunity and immunization in relation to public health
Online Access:https://eprints.ums.edu.my/id/eprint/34290/1/Abstract.pdf
https://eprints.ums.edu.my/id/eprint/34290/2/Full%20text.pdf
https://eprints.ums.edu.my/id/eprint/34290/
https://www.mdpi.com/2227-9032/10/6/994/htm
https://doi.org/10.3390/healthcare10060994
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spelling my.ums.eprints.342902022-09-27T01:19:54Z https://eprints.ums.edu.my/id/eprint/34290/ Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia Ong, Song Quan Maisarah Mohamed Pauzi Keng, Hoon Gan RA638 Immunity and immunization in relation to public health Vaccination is the primary preventive measure against the COVID-19 infection, and an additional vaccine dosage is crucial to increase the immunity level of the community. However, public bias, as reflected on social media, may have a significant impact on the vaccination program. We aim to investigate the attitudes to the COVID-19 vaccination booster in Malaysia by using sentiment analysis. We retrieved 788 tweets containing COVID-19 vaccine booster keywords and identified the common topics discussed in tweets that related to the booster by using latent Dirichlet allocation (LDA) and performed sentiment analysis to understand the determinants for the sentiments to receiving the vaccination booster in Malaysia. We identified three important LDA topics: (1) type of vaccination booster; (2) effects of vaccination booster; (3) vaccination program operation. The type of vaccination further transformed into attributes of “az”, “pfizer”, “sinovac”, and “mix” for determinants’ assessments. Effect and type of vaccine booster associated stronger than program operation topic for the sentiments, and “pfizer” and “mix” were the strongest determinants of the tweet’s sentiments after the Boruta feature selection and validated from the performance of regression analysis. This study provided a comprehensive workflow to retrieve and identify important healthcare topic from social media. Multidisciplinary Digital Publishing Institute 2022 Article PeerReviewed text en https://eprints.ums.edu.my/id/eprint/34290/1/Abstract.pdf text en https://eprints.ums.edu.my/id/eprint/34290/2/Full%20text.pdf Ong, Song Quan and Maisarah Mohamed Pauzi and Keng, Hoon Gan (2022) Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia. Healthcare, 10. pp. 1-12. ISSN 2227-9032 https://www.mdpi.com/2227-9032/10/6/994/htm https://doi.org/10.3390/healthcare10060994
institution Universiti Malaysia Sabah
building UMS Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Sabah
content_source UMS Institutional Repository
url_provider http://eprints.ums.edu.my/
language English
English
topic RA638 Immunity and immunization in relation to public health
spellingShingle RA638 Immunity and immunization in relation to public health
Ong, Song Quan
Maisarah Mohamed Pauzi
Keng, Hoon Gan
Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia
description Vaccination is the primary preventive measure against the COVID-19 infection, and an additional vaccine dosage is crucial to increase the immunity level of the community. However, public bias, as reflected on social media, may have a significant impact on the vaccination program. We aim to investigate the attitudes to the COVID-19 vaccination booster in Malaysia by using sentiment analysis. We retrieved 788 tweets containing COVID-19 vaccine booster keywords and identified the common topics discussed in tweets that related to the booster by using latent Dirichlet allocation (LDA) and performed sentiment analysis to understand the determinants for the sentiments to receiving the vaccination booster in Malaysia. We identified three important LDA topics: (1) type of vaccination booster; (2) effects of vaccination booster; (3) vaccination program operation. The type of vaccination further transformed into attributes of “az”, “pfizer”, “sinovac”, and “mix” for determinants’ assessments. Effect and type of vaccine booster associated stronger than program operation topic for the sentiments, and “pfizer” and “mix” were the strongest determinants of the tweet’s sentiments after the Boruta feature selection and validated from the performance of regression analysis. This study provided a comprehensive workflow to retrieve and identify important healthcare topic from social media.
format Article
author Ong, Song Quan
Maisarah Mohamed Pauzi
Keng, Hoon Gan
author_facet Ong, Song Quan
Maisarah Mohamed Pauzi
Keng, Hoon Gan
author_sort Ong, Song Quan
title Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia
title_short Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia
title_full Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia
title_fullStr Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia
title_full_unstemmed Text Mining and Determinants of Sentiments towards the COVID-19 Vaccine Booster of Twitter Users in Malaysia
title_sort text mining and determinants of sentiments towards the covid-19 vaccine booster of twitter users in malaysia
publisher Multidisciplinary Digital Publishing Institute
publishDate 2022
url https://eprints.ums.edu.my/id/eprint/34290/1/Abstract.pdf
https://eprints.ums.edu.my/id/eprint/34290/2/Full%20text.pdf
https://eprints.ums.edu.my/id/eprint/34290/
https://www.mdpi.com/2227-9032/10/6/994/htm
https://doi.org/10.3390/healthcare10060994
_version_ 1760231275404197888
score 13.211869

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