Sentiment Analysis of Covid-19 Vaccination using Support Vector Machine in Indonesia

Along with the development of the Covid-19 pandemic, many responses and news were shared through social media. The new Covid-19 vaccination promoted by the government has raised pros and cons from the public. Public resistance to covid-19 vaccination will lead to a higher fatality rate. This study carried out sentiment analysis about the Covid-19 vaccine using the Support Vector Machine (SVM). This research aims to study the public response to the acceptance of the vaccination program. The research result can be used to determine the direction of government policy. Data collection was taken via Twitter in the year 2021. The data then undergoes the preprocessing methods. Afterward, the data is processed using SVM classification. Finally, the result is evaluated by a confusion matrix. The experimental result shows that SVM produces 56.80% positive, 33.75% neutral, and 9.45% negative. The highest model accuracy was obtained by RBF kernel of 92%, linear and polynomial kernels obtained 90% accuracy, and sigmoid kernel obtained 89% accuracy.

@article{Rahardi2022,
  title     = {Sentiment Analysis of Covid-19 Vaccination using Support Vector Machine in Indonesia},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {13},
  number    = {6},
  year      = {2022},
  publisher = {The Science and Information Organization},
  author    = {Majid Rahardi and Afrig Aminuddin and Ferian Fauzi Abdulloh and Rizky Adhi Nugroho},
  doi       = {10.14569/IJACSA.2022.0130665},
  url       = {https://doi.org/10.14569/IJACSA.2022.0130665}
}