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Analysis of public sentiment on Covid-19 vaccination policy based on text mining with the Naïve Bayes Classifier approach

Link to publisher's homepage at https://amci.unimap.edu.my/

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Main Authors: Rita, Susanti, Alvito Aryo Pangestu, Haydar Arsy Firdaus, M. Fariz Fadillah Mardianto
Other Authors: m.fariz.fadillah.m@fst.unair.ac.id
Format: Article
Language:English
Published: Institute of Engineering Mathematics, Universiti Malaysia Perlis 2022
Subjects:
Naïve Bayes Classifier
Sentiment
Twitter
Text Mining
Online Access:http://dspace.unimap.edu.my:80/xmlui/handle/123456789/74084
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spelling my.unimap-740842022-02-11T12:11:26Z Analysis of public sentiment on Covid-19 vaccination policy based on text mining with the Naïve Bayes Classifier approach Rita, Susanti Alvito Aryo Pangestu Haydar Arsy Firdaus M. Fariz Fadillah Mardianto m.fariz.fadillah.m@fst.unair.ac.id Naïve Bayes Classifier Sentiment Twitter Text Mining Link to publisher's homepage at https://amci.unimap.edu.my/ One of the goals in the SDGs, which is to ensure a healthy life and promote the welfare of all people of all ages, has become difficult to maintain since the emergence of Covid-19 in Indonesia. Thus, the Indonesian government has issued a policy regarding the procurement of vaccines and the implementation of vaccinations through Presidential Regulation Number 99 of 2020. Meanwhile, the public's perception of the Covid-19 vaccine that appears are varies and will affect the Covid-19 vaccination process in Indonesia, so a sentiment analysis needs to be carried out to free Indonesia from the Covid-19 pandemic. By using the text mining method, the primary data collected is in the form of public opinions from Twitter. With the Naïve Bayes Classifier approach, it is concluded that the model is consistent and good enough to be used to classify public sentiment regarding the Covid-19 vaccination policy. 2022-02-11T12:11:26Z 2022-02-11T12:11:26Z 2021-12 Article Applied Mathematics and Computational Intelligence (AMCI), vol.10(1), 2021, pages 309-318 2289-1315 (print) 2289-1323 (online) http://dspace.unimap.edu.my:80/xmlui/handle/123456789/74084 en Institute of Engineering Mathematics, Universiti Malaysia Perlis
institution Universiti Malaysia Perlis
building UniMAP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Perlis
content_source UniMAP Library Digital Repository
url_provider http://dspace.unimap.edu.my/
language English
topic Naïve Bayes Classifier
Sentiment
Twitter
Text Mining
spellingShingle Naïve Bayes Classifier
Sentiment
Twitter
Text Mining
Rita, Susanti
Alvito Aryo Pangestu
Haydar Arsy Firdaus
M. Fariz Fadillah Mardianto
Analysis of public sentiment on Covid-19 vaccination policy based on text mining with the Naïve Bayes Classifier approach
description Link to publisher's homepage at https://amci.unimap.edu.my/
author2 m.fariz.fadillah.m@fst.unair.ac.id
author_facet m.fariz.fadillah.m@fst.unair.ac.id
Rita, Susanti
Alvito Aryo Pangestu
Haydar Arsy Firdaus
M. Fariz Fadillah Mardianto
format Article
author Rita, Susanti
Alvito Aryo Pangestu
Haydar Arsy Firdaus
M. Fariz Fadillah Mardianto
author_sort Rita, Susanti
title Analysis of public sentiment on Covid-19 vaccination policy based on text mining with the Naïve Bayes Classifier approach
title_short Analysis of public sentiment on Covid-19 vaccination policy based on text mining with the Naïve Bayes Classifier approach
title_full Analysis of public sentiment on Covid-19 vaccination policy based on text mining with the Naïve Bayes Classifier approach
title_fullStr Analysis of public sentiment on Covid-19 vaccination policy based on text mining with the Naïve Bayes Classifier approach
title_full_unstemmed Analysis of public sentiment on Covid-19 vaccination policy based on text mining with the Naïve Bayes Classifier approach
title_sort analysis of public sentiment on covid-19 vaccination policy based on text mining with the naïve bayes classifier approach
publisher Institute of Engineering Mathematics, Universiti Malaysia Perlis
publishDate 2022
url http://dspace.unimap.edu.my:80/xmlui/handle/123456789/74084
_version_ 1729704724382351360
score 13.222552

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