Submission Type
Poster
Start Date
4-26-2021
Abstract
The outbreak of Coronavirus Disease 2019 (COVID-19) has spread and affected many countries, causing global attention and concern. Understanding the underlying sentiment of a disease outbreak can help to keep track of spreading epidemics and provide a potential explanation for associated human behaviors. Social media, i.e., Twitter can serve as an important source to provide real-time information. Utilizing sentiment analysis, analysis of opinions can be gathered through Twitter. Therefore, performing sentiment analysis on the tweets related to the disease gives a better insight on the impact of the COVID-19 in our society. This study sheds light on how partisan preference affect individuals’ sentiments. AFINN lexicon analysis has been used to rate sentiment score of each tweet, which shows that Democrats tend to obtain higher positive sentiment compared to Republicans. To support this claim, a word analysis was also conducted to identify that Republicans express more negative sentiment over words pertaining to social distancing rules than Democrats. These findings may provide a better understanding of the development of public discourse on social media and the difference in pattern of behaviors in compliance to physical distancing rules according to an individual’s partisan identity
Recommended Citation
Kim, Won Seok and Weng, Weizhe, "011— Assessing 2019 Novel Coronavirus (COVID-19) Related Sentiment: Insights from Twitter Posts" (2021). GREAT Day Posters. 27.
https://knightscholar.geneseo.edu/great-day-symposium/great-day-2021/posters-2021/27
011— Assessing 2019 Novel Coronavirus (COVID-19) Related Sentiment: Insights from Twitter Posts
The outbreak of Coronavirus Disease 2019 (COVID-19) has spread and affected many countries, causing global attention and concern. Understanding the underlying sentiment of a disease outbreak can help to keep track of spreading epidemics and provide a potential explanation for associated human behaviors. Social media, i.e., Twitter can serve as an important source to provide real-time information. Utilizing sentiment analysis, analysis of opinions can be gathered through Twitter. Therefore, performing sentiment analysis on the tweets related to the disease gives a better insight on the impact of the COVID-19 in our society. This study sheds light on how partisan preference affect individuals’ sentiments. AFINN lexicon analysis has been used to rate sentiment score of each tweet, which shows that Democrats tend to obtain higher positive sentiment compared to Republicans. To support this claim, a word analysis was also conducted to identify that Republicans express more negative sentiment over words pertaining to social distancing rules than Democrats. These findings may provide a better understanding of the development of public discourse on social media and the difference in pattern of behaviors in compliance to physical distancing rules according to an individual’s partisan identity
Comments
Sponsored by Weizhe Weng. This project was supported by the Geneseo Foundation Undergraduate Full-time Fellowships.