The Spatial and Sentiment Analysis of Public Opinion Toward Covid-19 Pandemic Using Twitter Data: At the Early Stage of Vaccination
25 Pages Posted: 16 Mar 2022
Abstract
Social media, like Twitter, during the Coronavirus pandemic, is the platform that people have been able to share their opinions and obtain information. The present study provides a detailed spatial-temporal analysis of the Twitter online discourse (approximately 280 thousand tweets) in Ohio and Michigan at the early stage of vaccination (January 2021, till March 2021). This work aims to explore how people were feeling about the pandemic, what are the most frequent topics people were talking about, and how the topics spatially distributed. Moreover, state government responses and important news were gathered to analyze their impacts on public opinion based on the temporal analysis of the tweets. The natural language processing using the LDA method was deployed to identify 11 topics and 8 sub-topics of the Twitter data. The temporal analysis of topics shows their sensitivity to the significant state news and the local government's reactions to the pandemic. Moreover, the spatial distribution of Coronavirus-related tweets and sentiments demonstrates the concentration in the more populated urban areas with a high rate of COVID-19 cases and economic development in Ohio and Michigan. The government's economic responses to the pandemic, the vaccination timeline phases specified by each state, and the pandemic-related information can contribute to public opinion and sentiment trends. The findings of this study can aid the policymakers in such a crisis to understand public demands, and reactions, to follow the impacts of their policies at the county level on public opinions, and to manage their future responses to the pandemic.
Keywords: Coronavirus Pandemic, social media, Public opinion, Spatial analysis, Topic modeling, Sentiment analysis
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