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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Institute of Engineering Mathematics, Universiti Malaysia Perlis
2022
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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 Text Mining |
| spellingShingle |
Naïve Bayes Classifier Sentiment 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 |
13.252575 |