Sentiment Analysis on USA vs. New Zealand on Health and Safety Mandates During Early Stages of COVID-19 Pandemic

Dales, Joshua; Mirza, Farhaan; Adel, Amr

doi:10.3233/SHTI210575

Sentiment Analysis on USA vs. New Zealand on Health and Safety Mandates During Early Stages of COVID-19 Pandemic

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Date

2021-10-27

Authors

Dales, Joshua

Mirza, Farhaan

Adel, Amr

Item type

Journal Article

Publisher

IOS Press

Abstract

The Coronavirus pandemic has surprised the world and social media was extremely used to express frustrations and development of the cases found. Social media tools, such as Twitter, show a comparable impact with the number of tweets related to COVID-19 indicating remarkable development in a limited ability to focus time. The purpose of this paper is to investigate the impact of Coronavirus on the United States of America (USA) and New Zealand (NZ), and how that is reflected in a sentiment analysis through the examination of American and New Zealand tweets. We have gathered tweets from a March 2020 - August 2020 and used sentiment extraction on the tweets. The major finding of this sentiment extraction is the fact that the overall average sentiment over the 5-month period stayed in a negative range in the USA and NZ. This paper aims to analyze these trends, identify patterns, and determine whether these trends were caused by the COVID-19 pandemic or outside sources. One trend that was analyzed was the spike of COVID-19 results in relation to the number of protests occurring in the USA.

Keywords

COVID-19, Coronavirus, Data Request, Sentiment Analysis, Twitter, 46 Information and Computing Sciences, 4601 Applied Computing, Emerging Infectious Diseases, Health Disparities and Racial or Ethnic Minority Health Research, Health Disparities, Behavioral and Social Science, Minority Health, Infectious Diseases, Coronaviruses Disparities and At-Risk Populations, Coronaviruses, 0807 Library and Information Studies, 1117 Public Health and Health Services, Medical Informatics, 4203 Health services and systems, 4601 Applied computing