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Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA

Twitter is a social media platform with more than 500 million users worldwide. It has become a tool for spreading the news, discussing ideas and comments on world events. Twitter is also an important source of health-related information, given the amount of news, opinions and information that is sha...

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Detalles Bibliográficos
Autores principales: Garcia, Klaifer, Berton, Lilian
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier B.V. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832522/
https://www.ncbi.nlm.nih.gov/pubmed/33519326
http://dx.doi.org/10.1016/j.asoc.2020.107057
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author Garcia, Klaifer
Berton, Lilian
author_facet Garcia, Klaifer
Berton, Lilian
author_sort Garcia, Klaifer
collection PubMed
description Twitter is a social media platform with more than 500 million users worldwide. It has become a tool for spreading the news, discussing ideas and comments on world events. Twitter is also an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. It is a challenge identifying interesting and useful content from large text-streams in different languages, few works have explored languages other than English. In this paper, we use topic identification and sentiment analysis to explore a large number of tweets in both countries with a high number of spreading and deaths by COVID-19, Brazil, and the USA. We employ 3,332,565 tweets in English and 3,155,277 tweets in Portuguese to compare and discuss the effectiveness of topic identification and sentiment analysis in both languages. We ranked ten topics and analyzed the content discussed on Twitter for four months providing an assessment of the discourse evolution over time. The topics we identified were representative of the news outlets during April and August in both countries. We contribute to the study of the Portuguese language, to the analysis of sentiment trends over a long period and their relation to announced news, and the comparison of the human behavior in two different geographical locations affected by this pandemic. It is important to understand public reactions, information dissemination and consensus building in all major forms, including social media in different countries.
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spelling pubmed-78325222021-01-26 Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA Garcia, Klaifer Berton, Lilian Appl Soft Comput Article Twitter is a social media platform with more than 500 million users worldwide. It has become a tool for spreading the news, discussing ideas and comments on world events. Twitter is also an important source of health-related information, given the amount of news, opinions and information that is shared by both citizens and official sources. It is a challenge identifying interesting and useful content from large text-streams in different languages, few works have explored languages other than English. In this paper, we use topic identification and sentiment analysis to explore a large number of tweets in both countries with a high number of spreading and deaths by COVID-19, Brazil, and the USA. We employ 3,332,565 tweets in English and 3,155,277 tweets in Portuguese to compare and discuss the effectiveness of topic identification and sentiment analysis in both languages. We ranked ten topics and analyzed the content discussed on Twitter for four months providing an assessment of the discourse evolution over time. The topics we identified were representative of the news outlets during April and August in both countries. We contribute to the study of the Portuguese language, to the analysis of sentiment trends over a long period and their relation to announced news, and the comparison of the human behavior in two different geographical locations affected by this pandemic. It is important to understand public reactions, information dissemination and consensus building in all major forms, including social media in different countries. Elsevier B.V. 2021-03 2020-12-26 /pmc/articles/PMC7832522/ /pubmed/33519326 http://dx.doi.org/10.1016/j.asoc.2020.107057 Text en © 2020 Elsevier B.V. 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
Garcia, Klaifer
Berton, Lilian
Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA
title Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA
title_full Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA
title_fullStr Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA
title_full_unstemmed Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA
title_short Topic detection and sentiment analysis in Twitter content related to COVID-19 from Brazil and the USA
title_sort topic detection and sentiment analysis in twitter content related to covid-19 from brazil and the usa
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7832522/
https://www.ncbi.nlm.nih.gov/pubmed/33519326
http://dx.doi.org/10.1016/j.asoc.2020.107057
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