What Do the Twitter Sentiments Say About the COVID-19 Vaccine?
Start Date
15-4-2021 4:00 PM
End Date
15-4-2021 4:15 PM
Student's Major
Computer Information Science
Student's College
Science, Engineering and Technology
Mentor's Name
Naseef Mansoor
Mentor's Department
Computer Information Science
Mentor's College
Science, Engineering and Technology
Description
The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions and individuals navigate through this pandemic. In this paper, we analyze and investigate the twitter sentiments toward COVID-19 vaccine. Starting from a publicly available twitter dataset on COVID-19 vaccine from Kaggle, we create a unified dataset containing data about public sentiments, sentiment scores, and COVID-19 cases for various U.S. states. To generate a sentiment scores from the tweets, we have applied a Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analyzer. These scores were then classified to positive, negative, and neutral sentiment classes using a simple threshold-based classifier. From our analysis, we observe that in our dataset around 41.93% of the tweets are positive, 17.64% tweets are negative, and 40.42% tweets are neutral. We also analyzed the data based on geographic locations of the tweets to answer the following questions - 1) Is there any relationship between the number of tweets and the number of COVID-19 cases? 2) Is there any shift in the public sentiment after the approval of the vaccine? Our analysis shows high correlation between the number of tweets and the number of COVID-19 cases as well as a decrease in negative sentiment after the approval of the vaccine.
What Do the Twitter Sentiments Say About the COVID-19 Vaccine?
The coronavirus disease (COVID-19) pandemic led to substantial public discussion. Understanding these discussions can help institutions and individuals navigate through this pandemic. In this paper, we analyze and investigate the twitter sentiments toward COVID-19 vaccine. Starting from a publicly available twitter dataset on COVID-19 vaccine from Kaggle, we create a unified dataset containing data about public sentiments, sentiment scores, and COVID-19 cases for various U.S. states. To generate a sentiment scores from the tweets, we have applied a Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analyzer. These scores were then classified to positive, negative, and neutral sentiment classes using a simple threshold-based classifier. From our analysis, we observe that in our dataset around 41.93% of the tweets are positive, 17.64% tweets are negative, and 40.42% tweets are neutral. We also analyzed the data based on geographic locations of the tweets to answer the following questions - 1) Is there any relationship between the number of tweets and the number of COVID-19 cases? 2) Is there any shift in the public sentiment after the approval of the vaccine? Our analysis shows high correlation between the number of tweets and the number of COVID-19 cases as well as a decrease in negative sentiment after the approval of the vaccine.
