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Comparing tweet sentiments in megacities using machine learning techniques: In the midst of COVID-19
COVID-19 was announced by the World Health Organization as a pandemic on March 11, 2020. Not only has COVID-19 struck the economy and public health, but it also has deep influences on people's feelings. Twitter, as an active social media, is a great database where we can investigate people'...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Elsevier Ltd.
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9756302/ https://www.ncbi.nlm.nih.gov/pubmed/36540864 http://dx.doi.org/10.1016/j.cities.2021.103273 |
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author | Yao, Zhirui Yang, Junyan Liu, Jialin Keith, Michael Guan, ChengHe |
author_facet | Yao, Zhirui Yang, Junyan Liu, Jialin Keith, Michael Guan, ChengHe |
author_sort | Yao, Zhirui |
collection | PubMed |
description | COVID-19 was announced by the World Health Organization as a pandemic on March 11, 2020. Not only has COVID-19 struck the economy and public health, but it also has deep influences on people's feelings. Twitter, as an active social media, is a great database where we can investigate people's sentiments during this pandemic. By conducting sentiment analysis on Tweets using advanced machine learning techniques, this study aims to investigate how public sentiments respond to the pandemic from March 2 to May 21, 2020 in New York City, Los Angeles, London, and another six global mega-cities. Results showed that across cities, negative and positive Tweet sentiment clustered around mid-March and early May, respectively. Furthermore, positive sentiments of Tweets from New York City and London were positively correlated with stricter quarantine measures, although this correlation was not significant in Los Angeles. Meanwhile, Tweet sentiments of all three cities did not exhibit a strong correlation with new cases and hospitalization. Last but not least, we provide a qualitative analysis of the reasons behind differences in correlations shown above, along with a discussion of the polarizing effect of public policies on Tweet sentiments. Thus, the results of this study imply that Tweet sentiment is more sensitive to quarantine orders than reported statistics of COVID-19, especially in populous megacities where public transportation is heavily relied upon, which calls for prompt and effective quarantine measures during contagious disease outbreaks. |
format | Online Article Text |
id | pubmed-9756302 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Elsevier Ltd. |
record_format | MEDLINE/PubMed |
spelling | pubmed-97563022022-12-16 Comparing tweet sentiments in megacities using machine learning techniques: In the midst of COVID-19 Yao, Zhirui Yang, Junyan Liu, Jialin Keith, Michael Guan, ChengHe Cities Article COVID-19 was announced by the World Health Organization as a pandemic on March 11, 2020. Not only has COVID-19 struck the economy and public health, but it also has deep influences on people's feelings. Twitter, as an active social media, is a great database where we can investigate people's sentiments during this pandemic. By conducting sentiment analysis on Tweets using advanced machine learning techniques, this study aims to investigate how public sentiments respond to the pandemic from March 2 to May 21, 2020 in New York City, Los Angeles, London, and another six global mega-cities. Results showed that across cities, negative and positive Tweet sentiment clustered around mid-March and early May, respectively. Furthermore, positive sentiments of Tweets from New York City and London were positively correlated with stricter quarantine measures, although this correlation was not significant in Los Angeles. Meanwhile, Tweet sentiments of all three cities did not exhibit a strong correlation with new cases and hospitalization. Last but not least, we provide a qualitative analysis of the reasons behind differences in correlations shown above, along with a discussion of the polarizing effect of public policies on Tweet sentiments. Thus, the results of this study imply that Tweet sentiment is more sensitive to quarantine orders than reported statistics of COVID-19, especially in populous megacities where public transportation is heavily relied upon, which calls for prompt and effective quarantine measures during contagious disease outbreaks. Elsevier Ltd. 2021-09 2021-06-04 /pmc/articles/PMC9756302/ /pubmed/36540864 http://dx.doi.org/10.1016/j.cities.2021.103273 Text en © 2021 Elsevier Ltd. All rights reserved. Since January 2020 Elsevier has created a COVID-19 resource centre with free information in English and Mandarin on the novel coronavirus COVID-19. The COVID-19 resource centre is hosted on Elsevier Connect, the company's public news and information website. Elsevier hereby grants permission to make all its COVID-19-related research that is available on the COVID-19 resource centre - including this research content - immediately available in PubMed Central and other publicly funded repositories, such as the WHO COVID database with rights for unrestricted research re-use and analyses in any form or by any means with acknowledgement of the original source. These permissions are granted for free by Elsevier for as long as the COVID-19 resource centre remains active. |
spellingShingle | Article Yao, Zhirui Yang, Junyan Liu, Jialin Keith, Michael Guan, ChengHe Comparing tweet sentiments in megacities using machine learning techniques: In the midst of COVID-19 |
title | Comparing tweet sentiments in megacities using machine learning techniques: In the midst of COVID-19 |
title_full | Comparing tweet sentiments in megacities using machine learning techniques: In the midst of COVID-19 |
title_fullStr | Comparing tweet sentiments in megacities using machine learning techniques: In the midst of COVID-19 |
title_full_unstemmed | Comparing tweet sentiments in megacities using machine learning techniques: In the midst of COVID-19 |
title_short | Comparing tweet sentiments in megacities using machine learning techniques: In the midst of COVID-19 |
title_sort | comparing tweet sentiments in megacities using machine learning techniques: in the midst of covid-19 |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9756302/ https://www.ncbi.nlm.nih.gov/pubmed/36540864 http://dx.doi.org/10.1016/j.cities.2021.103273 |
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