Sentiment Analysis and Topic Modeling on COVID-19 Vaccines using Twitter Data
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.
Proceedings - 2022 International Conference on Computational Science and Computational Intelligence, CSCI 2022
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Ojeda, Daniel; Landaverde, Eric; Huang, Ching Yu; and Kwak, Daehan, "Sentiment Analysis and Topic Modeling on COVID-19 Vaccines using Twitter Data" (2022). Kean Publications. 666.