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Online public opinion evaluation through the functional resonance analysis method and deep analysis

A conventional model of public opinion analysis is no longer suitable when the internet is the primary arena of information dissemination. Thus, a more practical approach is urgently needed to deal with this dynamic and complicated phenomenon of propagating public opinion. This paper proposes that t...

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Detalles Bibliográficos
Autores principales: Yu, Linxing, Chen, Huaming, Luo, Wenqi, Li, Chang
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719775/
https://www.ncbi.nlm.nih.gov/pubmed/34972099
http://dx.doi.org/10.1371/journal.pone.0261009
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author Yu, Linxing
Chen, Huaming
Luo, Wenqi
Li, Chang
author_facet Yu, Linxing
Chen, Huaming
Luo, Wenqi
Li, Chang
author_sort Yu, Linxing
collection PubMed
description A conventional model of public opinion analysis is no longer suitable when the internet is the primary arena of information dissemination. Thus, a more practical approach is urgently needed to deal with this dynamic and complicated phenomenon of propagating public opinion. This paper proposes that the outbreak of internet public opinion and its negative impacts, such as the occurrence of major security incidents, are a result of coupling and the complex interaction of many factors. The Functional Resonance Analysis Method model is composed of those factors and considers the stages of network information dissemination, the unique propagation rule, and textual sentiment resonance on the internet. Moreover, it is the first public opinion governance method that simultaneously highlights the complex system, functional identification, and functional resonance. It suggests a more effective method to shorten the dissipation time of negative public opinion and is a considerable improvement over previous models for risk-prediction. Based on resonance theory and deep learning, this study establishes public opinion resonance functions, which made it possible to analyze public opinion triggers and build a simulation model to explore the patterns of public opinion development through long-term data capture. The simulation results of the Functional Resonance Analysis Method suggest that the resonance in the model is consistent with the evolution of public opinion in real situations and that the components of the resonance of public opinion can be separated into eleven subjective factors and three objective factors. In addition, managing the subjective factors can significantly accelerate the dissipation of negative opinions.
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spelling pubmed-87197752022-01-01 Online public opinion evaluation through the functional resonance analysis method and deep analysis Yu, Linxing Chen, Huaming Luo, Wenqi Li, Chang PLoS One Research Article A conventional model of public opinion analysis is no longer suitable when the internet is the primary arena of information dissemination. Thus, a more practical approach is urgently needed to deal with this dynamic and complicated phenomenon of propagating public opinion. This paper proposes that the outbreak of internet public opinion and its negative impacts, such as the occurrence of major security incidents, are a result of coupling and the complex interaction of many factors. The Functional Resonance Analysis Method model is composed of those factors and considers the stages of network information dissemination, the unique propagation rule, and textual sentiment resonance on the internet. Moreover, it is the first public opinion governance method that simultaneously highlights the complex system, functional identification, and functional resonance. It suggests a more effective method to shorten the dissipation time of negative public opinion and is a considerable improvement over previous models for risk-prediction. Based on resonance theory and deep learning, this study establishes public opinion resonance functions, which made it possible to analyze public opinion triggers and build a simulation model to explore the patterns of public opinion development through long-term data capture. The simulation results of the Functional Resonance Analysis Method suggest that the resonance in the model is consistent with the evolution of public opinion in real situations and that the components of the resonance of public opinion can be separated into eleven subjective factors and three objective factors. In addition, managing the subjective factors can significantly accelerate the dissipation of negative opinions. Public Library of Science 2021-12-31 /pmc/articles/PMC8719775/ /pubmed/34972099 http://dx.doi.org/10.1371/journal.pone.0261009 Text en © 2021 Yu et al 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 author and source are credited.
spellingShingle Research Article
Yu, Linxing
Chen, Huaming
Luo, Wenqi
Li, Chang
Online public opinion evaluation through the functional resonance analysis method and deep analysis
title Online public opinion evaluation through the functional resonance analysis method and deep analysis
title_full Online public opinion evaluation through the functional resonance analysis method and deep analysis
title_fullStr Online public opinion evaluation through the functional resonance analysis method and deep analysis
title_full_unstemmed Online public opinion evaluation through the functional resonance analysis method and deep analysis
title_short Online public opinion evaluation through the functional resonance analysis method and deep analysis
title_sort online public opinion evaluation through the functional resonance analysis method and deep analysis
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8719775/
https://www.ncbi.nlm.nih.gov/pubmed/34972099
http://dx.doi.org/10.1371/journal.pone.0261009
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