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Empirical research on the evolution trend of heat and sentiment for emergencies

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Názov: Empirical research on the evolution trend of heat and sentiment for emergencies
Autori: Shihong Wu, Wei Yu, Yanxia Zhao, Hongyan Li, Jiatong Wang, Weiyan Yang, Yue Lin
Zdroj: International Journal of Cognitive Computing in Engineering, Vol 6, Iss, Pp 323-332 (2025)
Informácie o vydavateľovi: Elsevier BV, 2025.
Rok vydania: 2025
Predmety: Sentiment analysis, Time series, Internet public opinion, Electronic computers. Computer science, Science, Anomaly detection, QA75.5-76.95, Topic heat
Popis: Emergencies inflict heavy casualties, economic losses, ecological damage, and significant social harm to society. By segmenting information topics and analysing emotional shifts, we can identify corresponding real-world events and their impacts, thereby providing guidance for timely responses to emergencies. In the past, public opinion monitoring of emergencies was based mainly on single-topic detection or emotion analysis, which cannot comprehensively evaluate the evolution of public opinion. In this work, word segmentation is applied to video comments related to various emergency situations. By utilizing the co-word network and Louvain algorithm for theme division, along with sentiment analysis constructed through time series analysis of sentiment value changes for various emergencies employing the naive Bayes method, the evolution of public opinion is comprehensively assessed. As a result, the pivotal nodes in the evolution of public opinion are identified and the evolution process is divided into stages. Using this method, relevant management departments can effectively address the majority of public opinions for various types of emergencies, addressing them from the perspectives of prevention, adjustment, and recovery. This approach not only enhances rescue efficiency and strengthens safety management but also actively guides the evolution of public opinion, ultimately providing society with solid and reliable security safeguards.
Druh dokumentu: Article
Jazyk: English
ISSN: 2666-3074
DOI: 10.1016/j.ijcce.2025.01.006
Prístupová URL adresa: https://doaj.org/article/00c60fe1af904fce8394f9081a41fbdd
Rights: CC BY NC ND
Prístupové číslo: edsair.doi.dedup.....50c8d82e857abf579ac7da46215e6a67
Databáza: OpenAIRE
Popis
Abstrakt:Emergencies inflict heavy casualties, economic losses, ecological damage, and significant social harm to society. By segmenting information topics and analysing emotional shifts, we can identify corresponding real-world events and their impacts, thereby providing guidance for timely responses to emergencies. In the past, public opinion monitoring of emergencies was based mainly on single-topic detection or emotion analysis, which cannot comprehensively evaluate the evolution of public opinion. In this work, word segmentation is applied to video comments related to various emergency situations. By utilizing the co-word network and Louvain algorithm for theme division, along with sentiment analysis constructed through time series analysis of sentiment value changes for various emergencies employing the naive Bayes method, the evolution of public opinion is comprehensively assessed. As a result, the pivotal nodes in the evolution of public opinion are identified and the evolution process is divided into stages. Using this method, relevant management departments can effectively address the majority of public opinions for various types of emergencies, addressing them from the perspectives of prevention, adjustment, and recovery. This approach not only enhances rescue efficiency and strengthens safety management but also actively guides the evolution of public opinion, ultimately providing society with solid and reliable security safeguards.
ISSN:26663074
DOI:10.1016/j.ijcce.2025.01.006