A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates
When the COVID-19 pandemic hit, the use of vaccines was advertised as the end of the pandemic by the entire world. However, the chances of vaccination depended on the sentiments of society and individuals about the vaccine. People's acceptance of vaccines can change depending on conditions and...
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| Format: | Article |
| Language: | English |
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Politeknik Elektronika Negeri Surabaya
2023-06-01
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| Series: | Emitter: International Journal of Engineering Technology |
| Subjects: | |
| Online Access: | https://emitter.pens.ac.id/index.php/emitter/article/view/768 |
| _version_ | 1797773326107541504 |
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| author | Katon Suwida Muhammad Yusuf Kardawi Diana Purwitasari Fahril Mabahist |
| author_facet | Katon Suwida Muhammad Yusuf Kardawi Diana Purwitasari Fahril Mabahist |
| author_sort | Katon Suwida |
| collection | DOAJ |
| description |
When the COVID-19 pandemic hit, the use of vaccines was advertised as the end of the pandemic by the entire world. However, the chances of vaccination depended on the sentiments of society and individuals about the vaccine. People's acceptance of vaccines can change depending on conditions and events. Social media platforms such as Twitter can be used as a source of information to find out the conditions and attitudes of the community toward the program. By implementing a machine learning technique on the COVID-19 vaccine dataset, we hope to impact the classification result with text. This study suggests three distinct machine learning models for classifying texts of the COVID-19 vaccination, namely a model based on the first lexicon using the feature extraction method; second, using the word insertion technique to utilize distribution representation; and third, a combination model of distribution representation and feature extraction based on the lexicon. From the evaluation that has been carried out, we found that a combination of lexicon-based and distributional representation methods succeeded in giving the best results for classifying the level of acceptance of the COVID-19 vaccine in Indonesia with an accuracy score of 71.44% and an F1-score of 71.43%.
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| first_indexed | 2024-03-12T22:04:48Z |
| format | Article |
| id | doaj.art-1d02fc5743ef4c789b3eb95cdf1870a9 |
| institution | Directory Open Access Journal |
| issn | 2355-391X 2443-1168 |
| language | English |
| last_indexed | 2024-03-12T22:04:48Z |
| publishDate | 2023-06-01 |
| publisher | Politeknik Elektronika Negeri Surabaya |
| record_format | Article |
| series | Emitter: International Journal of Engineering Technology |
| spelling | doaj.art-1d02fc5743ef4c789b3eb95cdf1870a92023-07-24T19:32:06ZengPoliteknik Elektronika Negeri SurabayaEmitter: International Journal of Engineering Technology2355-391X2443-11682023-06-0111110.24003/emitter.v11i1.768A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance RatesKaton Suwida0Muhammad Yusuf Kardawi1Diana Purwitasari2Fahril Mabahist3Institut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh NopemberInstitut Teknologi Sepuluh Nopember When the COVID-19 pandemic hit, the use of vaccines was advertised as the end of the pandemic by the entire world. However, the chances of vaccination depended on the sentiments of society and individuals about the vaccine. People's acceptance of vaccines can change depending on conditions and events. Social media platforms such as Twitter can be used as a source of information to find out the conditions and attitudes of the community toward the program. By implementing a machine learning technique on the COVID-19 vaccine dataset, we hope to impact the classification result with text. This study suggests three distinct machine learning models for classifying texts of the COVID-19 vaccination, namely a model based on the first lexicon using the feature extraction method; second, using the word insertion technique to utilize distribution representation; and third, a combination model of distribution representation and feature extraction based on the lexicon. From the evaluation that has been carried out, we found that a combination of lexicon-based and distributional representation methods succeeded in giving the best results for classifying the level of acceptance of the COVID-19 vaccine in Indonesia with an accuracy score of 71.44% and an F1-score of 71.43%. https://emitter.pens.ac.id/index.php/emitter/article/view/768vaccinationtext classificationlexicon-baseddistributional representations |
| spellingShingle | Katon Suwida Muhammad Yusuf Kardawi Diana Purwitasari Fahril Mabahist A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates Emitter: International Journal of Engineering Technology vaccination text classification lexicon-based distributional representations |
| title | A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates |
| title_full | A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates |
| title_fullStr | A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates |
| title_full_unstemmed | A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates |
| title_short | A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates |
| title_sort | combination of lexicon based and distributional representations for classification of indonesian vaccine acceptance rates |
| topic | vaccination text classification lexicon-based distributional representations |
| url | https://emitter.pens.ac.id/index.php/emitter/article/view/768 |
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