Sentiment Analysis on Covid19 Vaccines in Indonesia: From the Perspective of Sinovac and Pfizer

Deden Ade Nurdeni, Indra Budi, Aris Budi Santoso

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

The Covid-19 pandemic that hit the world, including in Indonesia, had a significant impact. Casualties, economic downturn, extreme poverty, and major changes in education are still happening today. The presence of the Covid19 vaccine is new hope for mankind to end this pandemic situation. The emergence of two types of vaccines in Indonesia, Sinovac, and Pfizer, lead to different Indonesian society reactions. This study aims to do a sentiment analysis of the two types of vaccines on the Twitter platform. Data from October until November 2020 has been crawled and processed to see the citizen opinion. The dataset was split into two types: Sinovac and Pfizer dataset. Both datasets were labeled manually into three classes: positive, negative, and neutral. The results show that 77% of Tweets indicate the positive segments, while 19% represent negative, and 4% seem to be neutral for Sinovac. From the standpoint of Pfizer, the results were 81%, 17%, and 3% for positive, negative, and neutral, respectively. In terms of model performance evaluation, with 10-fold cross-validation, the highest average accuracy in the Sinovac dataset is Support Vector Machine with 85% accuracy. Furthermore, the Support Vector Machine classifier has a superior accuracy value of 78% in the Pfizer dataset compared to other classifiers.

Original languageEnglish
Title of host publication3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
EditorsRayner Alfred, Haviluddin Haviluddin, Aji Prasetya Wibawa, Joan Santoso, Fachrul Kurniawan, Hartarto Junaedi, Purnawansyah Purnawansyah, Endang Setyati, Herman Thuan To Saurik, Esther Irawati Setiawan, Eka Rahayu Setyaningsih, Edwin Pramana, Yosi Kristian, Kelvin Kelvin, Devi Dwi Purwanto, Eunike Kardinata, Prananda Anugrah
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages122-127
Number of pages6
ISBN (Electronic)9781665405140
DOIs
Publication statusPublished - 9 Apr 2021
Event3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021 - Virtual, Surabaya, Indonesia
Duration: 9 Apr 202111 Apr 2021

Publication series

Name3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021

Conference

Conference3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
Country/TerritoryIndonesia
CityVirtual, Surabaya
Period9/04/2111/04/21

Keywords

  • classification
  • Covid-19
  • machine learning
  • sentiment analysis
  • social media
  • text mining
  • Twitter
  • vaccines

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