Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2020 Aug 3:8:142173-142190.
doi: 10.1109/ACCESS.2020.3013933. eCollection 2020.

Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics

Affiliations

Feeling Positive About Reopening? New Normal Scenarios From COVID-19 US Reopen Sentiment Analytics

Jim Samuel et al. IEEE Access. .

Abstract

The Coronavirus pandemic has created complex challenges and adverse circumstances. This research identifies public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential public sentiment associated scenarios. The severity and brutality of COVID-19 have led to the development of extreme feelings, and emotional and mental healthcare challenges. This research focuses on emotional consequences - the presence of extreme fear, confusion and volatile sentiments, mixed along with trust and anticipation. It is necessary to gauge dominant public sentiment trends for effective decisions and policies. This study analyzes public sentiment using Twitter Data, time-aligned to the COVID-19 reopening debate, to identify dominant sentiment trends associated with the push to reopen the economy. Present research uses textual analytics methodologies to analyze public sentiment support for two potential divergent scenarios - an early opening and a delayed opening, and consequences of each. Present research concludes on the basis of textual data analytics, including textual data visualization and statistical validation, that tweets data from American Twitter users shows more positive sentiment support, than negative, for reopening the US economy. This research develops a novel sentiment polarity based public sentiment scenarios (PSS) framework, which will remain useful for future crises analysis, well beyond COVID-19. With additional validation, this research stream could present valuable time sensitive opportunities for state governments, the federal government, corporations and societal leaders to guide local and regional communities, and the nation into a successful new normal future.

Keywords: COVID-19; Twitter; coronavirus; feeling; new normal; reopen; sentiment analysis; textual analytics.

PubMed Disclaimer

Figures

FIGURE 1.
FIGURE 1.
Reopening public sentiment summary.
FIGURE 2.
FIGURE 2.
Wordcloud summary of tweets data.
FIGURE 3.
FIGURE 3.
Impact of COVID-19 outbreak on medical resources in the US (source, CDC [35]).
FIGURE 4.
FIGURE 4.
COVID-19 outbreak by states as of May 7, 2020 (source, worldometers [6]).
FIGURE 5.
FIGURE 5.
Unemployment situation in the US due to COVID-19 (source, US Department of Labor [7]).
FIGURE 6.
FIGURE 6.
N-Grams.
FIGURE 7.
FIGURE 7.
Tweets grouped by device type.
FIGURE 8.
FIGURE 8.
Progression of sentiments: trust, anticipation, sadness and fear.
FIGURE 9.
FIGURE 9.
Positive and negative sentiment about reopening.
FIGURE 10.
FIGURE 10.
Sentiment analysis of new normal scenarios.
FIGURE 11.
FIGURE 11.
Statistical analysis of sentiment values.

Similar articles

Cited by

References

    1. Coe E. H. and Enomoto K.. (2020). Returning to Resilience: The Impact of COVID-19 on Mental health and Substance Use. McKinsey & Company. Accessed: May 16, 2020. [Online]. Available: https://www.mckinsey.com/industries/healthcare-systems-and-services/our%...
    1. Goldmann E. and Galea S., “Mental health consequences of disasters,” Annu. Rev. Public Health, vol. 35, no. , pp. 169–183, Mar. 2014. - PubMed
    1. Waheed A., Goyal M., Gupta D., Khanna A., Al-Turjman F., and Pinheiro P. R., “CovidGAN: Data augmentation using auxiliary classifier GAN for improved covid-19 detection,” IEEE Access, vol. 8, pp. 91916–91923, 2020. - PMC - PubMed
    1. Zhong L., Mu L., Li J., Wang J., Yin Z., and Liu D., “Early prediction of the 2019 novel coronavirus outbreak in the mainland China based on simple mathematical model,” IEEE Access, vol. 8, pp. 51761–51769, 2020. - PMC - PubMed
    1. Moghadas S. M., Shoukat A., Fitzpatrick M. C., Wells C. R., Sah P., Pandey A., Sachs J. D., Wang Z., Meyers L. A., Singer B. H., and Galvani A. P., “Projecting hospital utilization during the COVID-19 outbreaks in the united states,” Proc. Nat. Acad. Sci. USA, vol. 117, no. 16, pp. 9122–9126, Apr. 2020. - PMC - PubMed

LinkOut - more resources