  Investigating and classifying sentiments of social media users towards an
item, situation, and system are very popular among the researchers. However,
they rarely discuss the underlying socioeconomic factor associations for such
sentiments. This study attempts to explore the factors associated with positive
and negative sentiments of the people about reopening the economy, in the
United States (US) amidst the COVID-19 global crisis. It takes into
consideration the situational uncertainties (i.e., changes in work and travel
pattern due to lockdown policies), economic downturn and associated trauma, and
emotional factors such as depression. To understand the sentiment of the people
about the reopening economy, Twitter data was collected, representing the 51
states including Washington DC of the US. State-wide socioeconomic
characteristics of the people, built environment data, and the number of
COVID-19 related cases were collected and integrated with Twitter data to
perform the analysis. A binary logit model was used to identify the factors
that influence people toward a positive or negative sentiment. The results from
the logit model demonstrate that family households, people with low education
levels, people in the labor force, low-income people, and people with higher
house rent are more interested in reopening the economy. In contrast,
households with a high number of members and high income are less interested to
reopen the economy. The model can correctly classify 56.18% of the sentiments.
The Pearson chi2 test indicates that overall this model has high
goodness-of-fit. This study provides a clear indication to the policymakers
where to allocate resources and what policy options they can undertake to
improve the socioeconomic situations of the people and mitigate the impacts of
pandemics in the current situation and as well as in the future.
