Cargando…

Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study

BACKGROUND: COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data. OBJECTIVE: The aim of this study was to i...

Descripción completa

Detalles Bibliográficos
Autores principales: Boon-Itt, Sakun, Skunkan, Yukolpat
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661106/
https://www.ncbi.nlm.nih.gov/pubmed/33108310
http://dx.doi.org/10.2196/21978
_version_ 1783609142313746432
author Boon-Itt, Sakun
Skunkan, Yukolpat
author_facet Boon-Itt, Sakun
Skunkan, Yukolpat
author_sort Boon-Itt, Sakun
collection PubMed
description BACKGROUND: COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data. OBJECTIVE: The aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic. METHODS: Data mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis. RESULTS: The results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19. CONCLUSIONS: Sentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease.
format Online
Article
Text
id pubmed-7661106
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher JMIR Publications
record_format MEDLINE/PubMed
spelling pubmed-76611062020-11-19 Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study Boon-Itt, Sakun Skunkan, Yukolpat JMIR Public Health Surveill Original Paper BACKGROUND: COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data. OBJECTIVE: The aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic. METHODS: Data mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis. RESULTS: The results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19. CONCLUSIONS: Sentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease. JMIR Publications 2020-11-11 /pmc/articles/PMC7661106/ /pubmed/33108310 http://dx.doi.org/10.2196/21978 Text en ©Sakun Boon-Itt, Yukolpat Skunkan. Originally published in JMIR Public Health and Surveillance (http://publichealth.jmir.org), 11.11.2020. https://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Public Health and Surveillance, is properly cited. The complete bibliographic information, a link to the original publication on http://publichealth.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Boon-Itt, Sakun
Skunkan, Yukolpat
Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study
title Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study
title_full Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study
title_fullStr Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study
title_full_unstemmed Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study
title_short Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study
title_sort public perception of the covid-19 pandemic on twitter: sentiment analysis and topic modeling study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7661106/
https://www.ncbi.nlm.nih.gov/pubmed/33108310
http://dx.doi.org/10.2196/21978
work_keys_str_mv AT boonittsakun publicperceptionofthecovid19pandemicontwittersentimentanalysisandtopicmodelingstudy
AT skunkanyukolpat publicperceptionofthecovid19pandemicontwittersentimentanalysisandtopicmodelingstudy