TY - GEN
T1 - Sentiment Analysis on Covid19 Vaccines in Indonesia
T2 - 3rd East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
AU - Nurdeni, Deden Ade
AU - Budi, Indra
AU - Santoso, Aris Budi
N1 - Publisher Copyright:
© 2021 IEEE.
PY - 2021/4/9
Y1 - 2021/4/9
N2 - 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.
AB - 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.
KW - classification
KW - Covid-19
KW - machine learning
KW - sentiment analysis
KW - social media
KW - text mining
KW - Twitter
KW - vaccines
UR - http://www.scopus.com/inward/record.url?scp=85107295899&partnerID=8YFLogxK
U2 - 10.1109/EIConCIT50028.2021.9431852
DO - 10.1109/EIConCIT50028.2021.9431852
M3 - Conference contribution
AN - SCOPUS:85107295899
T3 - 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
SP - 122
EP - 127
BT - 3rd 2021 East Indonesia Conference on Computer and Information Technology, EIConCIT 2021
A2 - Alfred, Rayner
A2 - Haviluddin, Haviluddin
A2 - Wibawa, Aji Prasetya
A2 - Santoso, Joan
A2 - Kurniawan, Fachrul
A2 - Junaedi, Hartarto
A2 - Purnawansyah, Purnawansyah
A2 - Setyati, Endang
A2 - Saurik, Herman Thuan To
A2 - Setiawan, Esther Irawati
A2 - Setyaningsih, Eka Rahayu
A2 - Pramana, Edwin
A2 - Kristian, Yosi
A2 - Kelvin, Kelvin
A2 - Purwanto, Devi Dwi
A2 - Kardinata, Eunike
A2 - Anugrah, Prananda
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 9 April 2021 through 11 April 2021
ER -