TY - GEN
T1 - Sentiment Analysis and Topic Modeling on COVID-19 Vaccines using Twitter Data
AU - Ojeda, Daniel
AU - Landaverde, Eric
AU - Huang, Ching Yu
AU - Kwak, Daehan
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - Ever since the public began quarantining due to Covid-19 in 2020, people have been waiting for life to go back to normal. Until April 2021, 'normal' activities were only allowed by being socially distant or wearing a mask. However, in early 2021, CDC announced that a vaccine would soon be released to the public. This announcement seemed to be good news for some, but for others, this was another obstacle on the way to normalcy. People have shown pushback against the Covid-19 vaccine due to the uncertainty of its effectiveness coupled with its potential side effects. Vaccine hesitancy has a negative impact on society and poses a real threat to public health. This is an important issue worldwide, and questions arise about how the general public feels about getting vaccinated. Therefore, the purpose of this study is to analyze the sentiment of Twitter users towards vaccination, specifically the Covid-19 vaccine. This study collects data through Twitter IDs to pick up on hashtags and keywords relating to the Covid-19 vaccine via the Twitter API and Tweepy. Tweets are put through a sentiment analysis tool to get a general idea of the sentiment. Furthermore, topic modeling is used to understand the topics discussed when mentioning the Covid-19 vaccine. By analyzing the sentiment towards the Covid-19 vaccine, we hope to provide the first step towards mitigating the risk associated with vaccine hesitancy.
AB - Ever since the public began quarantining due to Covid-19 in 2020, people have been waiting for life to go back to normal. Until April 2021, 'normal' activities were only allowed by being socially distant or wearing a mask. However, in early 2021, CDC announced that a vaccine would soon be released to the public. This announcement seemed to be good news for some, but for others, this was another obstacle on the way to normalcy. People have shown pushback against the Covid-19 vaccine due to the uncertainty of its effectiveness coupled with its potential side effects. Vaccine hesitancy has a negative impact on society and poses a real threat to public health. This is an important issue worldwide, and questions arise about how the general public feels about getting vaccinated. Therefore, the purpose of this study is to analyze the sentiment of Twitter users towards vaccination, specifically the Covid-19 vaccine. This study collects data through Twitter IDs to pick up on hashtags and keywords relating to the Covid-19 vaccine via the Twitter API and Tweepy. Tweets are put through a sentiment analysis tool to get a general idea of the sentiment. Furthermore, topic modeling is used to understand the topics discussed when mentioning the Covid-19 vaccine. By analyzing the sentiment towards the Covid-19 vaccine, we hope to provide the first step towards mitigating the risk associated with vaccine hesitancy.
KW - Covid-19
KW - Opinion Mining
KW - Sentiment Analysis
KW - Topic Modeling
KW - Vaccine
UR - http://www.scopus.com/inward/record.url?scp=85171978263&partnerID=8YFLogxK
U2 - 10.1109/CSCI58124.2022.00140
DO - 10.1109/CSCI58124.2022.00140
M3 - Conference contribution
AN - SCOPUS:85171978263
T3 - Proceedings - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022
SP - 767
EP - 771
BT - Proceedings - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022
Y2 - 14 December 2022 through 16 December 2022
ER -