Feeling Like It is Time to Reopen Now? COVID-19 New Normal Scenarios based on Reopening Sentiment Analytics

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.

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

“…The present research seeks to understand factors that support reopening, expressed through positive sentiment towards reopening. The goal of the present study is to extend past research which has demonstrated the prominence of positive sentiment towards reopening, though there exists a fair amount of negative sentiment as well [8]. Though Twitter data has been intrinsically analyzed extensively and also contextualized to numerous domains, yet past research has not combined recent Twitter data with demographic data to model potential relationships between sentiment classes based on Tweets and COVID-19 relevant data [15][16][17].…”

“…This study uses Twitter data collected between April 30, 2020, and May 08, 2020, to understand the sentiment of the people towards the reopening of the US economy [8]. A total of 293,597 tweets with 90 variables were downloaded using the keyword reopen.…”

“…COVID-19 was first identified in Wuhan, China, by the end of December 2019, already affected over 10 million people in 213 countries of the world with a fatality rate, that had reportedly almost reached 10% among the closed cases [6]. It is a highly infectious and deadly disease, with widespread transmission and significantly negative impacts on physical, emotional and mental health, economy, and peoples way of living [7,8]. To control the spread of COVID-19, federal government declared statewide emergency and states governments implemented stay-athome-order, imposed restriction on mass gathering and non-essential movements.…”

“…Past research investigated fear and trust sentiments of the people towards reopening the economy in the US using exploratory textual analytics, textual data visualization, and hypothesis testing techniques [8]. Similarly, past research has also investigated and classified sentiments of the users (e.g., positive, negative) towards an item, situation, and system [7].…”

“…The findings of the exploratory analysis using steps and processes described above helped to gain a clearer understanding of public perspectives on reopening [8]. After data exploration, sentiment score was generated for each tweet by using the R package sentimentr and sentiments were classified into positive, negative, and neutral based on matching keywords, word sequences, and prewritten lexicons.…”