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Text recognition and analysis of network public opinion focus events of a major epidemic: a case study of “COVID-19” in Sina Microblogs

Identifying and analyzing the public’s opinion of focal events during a major epidemic can help the government grasp the vicissitudes of network public opinion in a timely manner and provide the appropriate responses. Taking the COVID-19 epidemic as an example, this study begins by using Python-sele...

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Detalles Bibliográficos
Autores principales: Wei, HeLin, Hai, Chenying, Shan, Donglu, Lyu, Bei, Wang, Xiulai
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer US 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989565/
https://www.ncbi.nlm.nih.gov/pubmed/37362683
http://dx.doi.org/10.1007/s11042-023-14916-x
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author Wei, HeLin
Hai, Chenying
Shan, Donglu
Lyu, Bei
Wang, Xiulai
author_facet Wei, HeLin
Hai, Chenying
Shan, Donglu
Lyu, Bei
Wang, Xiulai
author_sort Wei, HeLin
collection PubMed
description Identifying and analyzing the public’s opinion of focal events during a major epidemic can help the government grasp the vicissitudes of network public opinion in a timely manner and provide the appropriate responses. Taking the COVID-19 epidemic as an example, this study begins by using Python-selenium to capture the original text and comment data related to COVID-19 from Sina Microblog’s CCTV News from Jan. 19, 2020, to Feb. 20, 2020. The study subsequently uses a manual interpretation method to classify the Weibo content and analyzes the shifting focus phenomena of network public opinion based on the moving average method. Next, the study uses an enhances TF-IDF to extract keywords from the Weibo comment and uses the keywords to construct a word co-occurrence network. The results show that during the epidemic, the network public opinion focus shifted significantly over time. With the progression of the epidemic, the focus of network public opinion diversified, and various categories stabilized. Compared to simple keyword and text classification recognition focus problems, the proposed model, which is highly accurate, identified multiple network public opinion focus problems and described the core contradictions of the different focus problems.
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spelling pubmed-99895652023-03-07 Text recognition and analysis of network public opinion focus events of a major epidemic: a case study of “COVID-19” in Sina Microblogs Wei, HeLin Hai, Chenying Shan, Donglu Lyu, Bei Wang, Xiulai Multimed Tools Appl Article Identifying and analyzing the public’s opinion of focal events during a major epidemic can help the government grasp the vicissitudes of network public opinion in a timely manner and provide the appropriate responses. Taking the COVID-19 epidemic as an example, this study begins by using Python-selenium to capture the original text and comment data related to COVID-19 from Sina Microblog’s CCTV News from Jan. 19, 2020, to Feb. 20, 2020. The study subsequently uses a manual interpretation method to classify the Weibo content and analyzes the shifting focus phenomena of network public opinion based on the moving average method. Next, the study uses an enhances TF-IDF to extract keywords from the Weibo comment and uses the keywords to construct a word co-occurrence network. The results show that during the epidemic, the network public opinion focus shifted significantly over time. With the progression of the epidemic, the focus of network public opinion diversified, and various categories stabilized. Compared to simple keyword and text classification recognition focus problems, the proposed model, which is highly accurate, identified multiple network public opinion focus problems and described the core contradictions of the different focus problems. Springer US 2023-03-07 /pmc/articles/PMC9989565/ /pubmed/37362683 http://dx.doi.org/10.1007/s11042-023-14916-x Text en © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023, Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law. This article is made available via the PMC Open Access Subset for unrestricted research re-use and secondary analysis in any form or by any means with acknowledgement of the original source. These permissions are granted for the duration of the World Health Organization (WHO) declaration of COVID-19 as a global pandemic.
spellingShingle Article
Wei, HeLin
Hai, Chenying
Shan, Donglu
Lyu, Bei
Wang, Xiulai
Text recognition and analysis of network public opinion focus events of a major epidemic: a case study of “COVID-19” in Sina Microblogs
title Text recognition and analysis of network public opinion focus events of a major epidemic: a case study of “COVID-19” in Sina Microblogs
title_full Text recognition and analysis of network public opinion focus events of a major epidemic: a case study of “COVID-19” in Sina Microblogs
title_fullStr Text recognition and analysis of network public opinion focus events of a major epidemic: a case study of “COVID-19” in Sina Microblogs
title_full_unstemmed Text recognition and analysis of network public opinion focus events of a major epidemic: a case study of “COVID-19” in Sina Microblogs
title_short Text recognition and analysis of network public opinion focus events of a major epidemic: a case study of “COVID-19” in Sina Microblogs
title_sort text recognition and analysis of network public opinion focus events of a major epidemic: a case study of “covid-19” in sina microblogs
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9989565/
https://www.ncbi.nlm.nih.gov/pubmed/37362683
http://dx.doi.org/10.1007/s11042-023-14916-x
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