  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 (ML) 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.
