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Investigating and classifying sentiments of social media users towards anitem, situation, and system are very popular among the researchers. However,they rarely discuss the underlying socioeconomic factor associations for suchsentiments. This study attempts to explore the factors associated with positiveand negative sentiments of the people about reopening the economy, in theUnited States (US) amidst the COVID-19 global crisis. It takes intoconsideration the situational uncertainties (i.e., changes in work and travelpattern due to lockdown policies), economic downturn and associated trauma, andemotional factors such as depression. To understand the sentiment of the peopleabout the reopening economy, Twitter data was collected, representing the 51states including Washington DC of the US. State-wide socioeconomiccharacteristics of the people, built environment data, and the number ofCOVID-19 related cases were collected and integrated with Twitter data toperform the analysis. A binary logit model was used to identify the factorsthat influence people toward a positive or negative sentiment. The results fromthe logit model demonstrate that family households, people with low educationlevels, people in the labor force, low-income people, and people with higherhouse rent are more interested in reopening the economy. In contrast,households with a high number of members and high income are less interested toreopen the economy. The model can correctly classify 56.18% of the sentiments.The Pearson chi2 test indicates that overall this model has highgoodness-of-fit. This study provides a clear indication to the policymakerswhere to allocate resources and what policy options they can undertake toimprove the socioeconomic situations of the people and mitigate the impacts ofpandemics in the current situation and as well as in the future.

Twitter and Census Data Analytics to Explore Socioeconomic Factors for Post-COVID-19 Reopening Sentiment