  The human severe acute respiratory syndrome coronavirus 2 (SARS-Cov-2),
causing the COVID-19 disease, has continued to spread all over the world. It
menacingly affects not only public health and global economics but also mental
health and mood. While the impact of the COVID-19 pandemic has been widely
studied, relatively fewer discussions about the sentimental reaction of the
population have been available. In this article, we scrape COVID-19 related
tweets on the microblogging platform, Twitter, and examine the tweets from
Feb~24, 2020 to Oct~14, 2020 in four Canadian cities (Toronto, Montreal,
Vancouver, and Calgary) and four U.S. cities (New York, Los Angeles, Chicago,
and Seattle). Applying the Vader and NRC approaches, we evaluate the sentiment
intensity scores and visualize the information over different periods of the
pandemic. Sentiment scores for the tweets concerning three anti-epidemic
measures, masks, vaccine, and lockdown, are computed for comparisons. The
results of four Canadian cities are compared with four cities in the United
States. We study the causal relationships between the infected cases, the tweet
activities, and the sentiment scores of COVID-19 related tweets, by integrating
the echo state network method with convergent cross-mapping. Our analysis shows
that public sentiments regarding COVID-19 vary in different time periods and
locations. In general, people have a positive mood about COVID-19 and masks,
but negative in the topics of vaccine and lockdown. The causal inference shows
that the sentiment influences people's activities on Twitter, which is also
correlated to the daily number of infections.
