Abstract
Along with the Coronavirus pandemic, another crisis has manifested itself in the form of mass fear and panic phenomena, fueled by incomplete and often inaccurate information. There is therefore a tremendous need to address and better understand COVID-19’s informational crisis and gauge public sentiment, so that appropriate messaging and policy decisions can be implemented. In this research article, we identify public sentiment associated with the pandemic using Coronavirus specific Tweets and R statistical software, along with its sentiment analysis packages. We demonstrate insights into the progress of fear-sentiment over time as COVID-19 approached peak levels in the United States, using descriptive textual analytics supported by necessary textual data visualizations. Furthermore, we provide a methodological overview of two essential machine learning classification methods, in the context of textual analytics, and compare their effectiveness in classifying Coronavirus Tweets of varying lengths. We observe a strong classification accuracy of 91% for short Tweets, with the Naive Bayes method. We also observe that the logistic regression classification method provides a reasonable accuracy of 74% with shorter Tweets, and both methods showed relatively weaker performance for longer Tweets. This research provides insights into Coronavirus fear sentiment progression, and outlines associated methods, implications, limitations and opportunities.
Highlights
We cover four critical issues: (1) public sentiment associated with the progress of Coronavirus and COVID-19, (2) the use of Twitter data, namely Tweets, for sentiment analysis, (3) descriptive textual analytics and textual data visualization, and (4) comparison of textual classification mechanisms used in artificial intelligence (AI)
We addressed issues surrounding public sentiment reflecting deep concerns about Coronavirus and COVID-19, leading to the identification of growth in fear sentiment and negative sentiment
We demonstrated the use of exploratory and descriptive textual analytics and textual data visualization methods, to discover early stage insights, such as by grouping of words by levels of a specific non-text variable
Summary
We cover four critical issues: (1) public sentiment associated with the progress of Coronavirus and COVID-19, (2) the use of Twitter data, namely Tweets, for sentiment analysis, (3) descriptive textual analytics and textual data visualization, and (4) comparison of textual classification mechanisms used in artificial intelligence (AI). The rapid spread of Coronavirus and COVID-19 infections have created a strong need for discovering efficient analytics methods for understanding the flow of information and the development of mass sentiment in pandemic scenarios. Information 2020, 11, 314 for COVID-19 management and economic recovery scenarios. In their industry-oriented report, they emphasized data management, tracking and informational dashboards as critical components of managing a wide range of COVID-19 scenarios. There has been an exponential growth in the use of textual analytics, natural language processing (NLP) and other artificial intelligence techniques in research and in the development of applications
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