Version 1
: Received: 19 May 2020 / Approved: 20 May 2020 / Online: 20 May 2020 (04:19:58 CEST)
How to cite:
Samuel, J.; Rahman, M. M.; Ali, G. G. M. N.; Samuel, Y.; Pelaez, A. Feeling Like It Is Time to Reopen Now? COVID-19 New Normal Scenarios Based on Reopening Sentiment Analytics. Preprints2020, 2020050318. https://doi.org/10.20944/preprints202005.0318.v1
Samuel, J.; Rahman, M. M.; Ali, G. G. M. N.; Samuel, Y.; Pelaez, A. Feeling Like It Is Time to Reopen Now? COVID-19 New Normal Scenarios Based on Reopening Sentiment Analytics. Preprints 2020, 2020050318. https://doi.org/10.20944/preprints202005.0318.v1
Samuel, J.; Rahman, M. M.; Ali, G. G. M. N.; Samuel, Y.; Pelaez, A. Feeling Like It Is Time to Reopen Now? COVID-19 New Normal Scenarios Based on Reopening Sentiment Analytics. Preprints2020, 2020050318. https://doi.org/10.20944/preprints202005.0318.v1
APA Style
Samuel, J., Rahman, M. M., Ali, G. G. M. N., Samuel, Y., & Pelaez, A. (2020). Feeling Like It Is Time to Reopen Now? COVID-19 New Normal Scenarios Based on Reopening Sentiment Analytics. Preprints. https://doi.org/10.20944/preprints202005.0318.v1
Chicago/Turabian Style
Samuel, J., Yana Samuel and Alexander Pelaez. 2020 "Feeling Like It Is Time to Reopen Now? COVID-19 New Normal Scenarios Based on Reopening Sentiment Analytics" Preprints. https://doi.org/10.20944/preprints202005.0318.v1
Abstract
The Coronavirus pandemic has created complex challenges and adverse circumstances. This research discovers public sentiment amidst problematic socioeconomic consequences of the lockdown, and explores ensuing four potential 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 identifies 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 COVID-19, 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 exploratory textual analytics and textual data visualization, that Tweets data from American Twitter users shows more trust sentiment support, than fear, for reopening the US economy. With additional validation, this could present a valuable time sensitive opportunity for state governments, the federal government, corporations and societal leaders to guide the nation into a successful new normal future.
Computer Science and Mathematics, Information Systems
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
