A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates

  • Katon Suwida Institut Teknologi Sepuluh Nopember, Indonesia
  • Muhammad Yusuf Kardawi Institut Teknologi Sepuluh Nopember, Indonesia
  • Diana Purwitasari Institut Teknologi Sepuluh Nopember, Indonesia
  • Fahril Mabahist Institut Teknologi Sepuluh Nopember, Indonesia
Keywords: vaccination, text classification, lexicon-based, distributional representations


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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How to Cite
Suwida, K., Kardawi, M. Y., Purwitasari, D., & Mabahist, F. (2023). A Combination of Lexicon-based and Distributional Representations for Classification of Indonesian Vaccine Acceptance Rates. EMITTER International Journal of Engineering Technology, 11(1), 89-99. https://doi.org/10.24003/emitter.v11i1.768