Legal Early Warning of Public Crisis in Network Public Opinion Events Based on Emotional Tendency.
At present, China is in the period of social transformation, and social contradictions are gradually prominent. The research on NPO (network public opinion) emergency warning methods is gradually increasing. Some existing laws and regulations are abstracted and principled in content, lacking specifi...
| Publicado en: | Journal of Environmental & Public Health pp. 1 - 10 |
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| Autor principal: | |
| Formato: | algorithm equations & formulas research tables/charts Journal Article |
| Publicado: |
Wiley-Blackwell
8/23/2022
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=158676762&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 158676762 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 16879805 9034 jtl: Journal of Environmental & Public Health issn: 16879805 maglogo: N pubinfo: dt: 8/23/2022 pid: 480 pub: Wiley-Blackwell place: Malden, Massachusetts artinfo: ui: 158676762 158676762 NLM36052349 158676762 10.1155/2022/6367295 NLM36052349 158676762 ppf: 1 ppct: 9 formats: tig: atl: Legal Early Warning of Public Crisis in Network Public Opinion Events Based on Emotional Tendency. aug: au: Ni, Zhijuan affil: School of Political Science and Law, Weifang University, Weifang, Shandong 261061, China sug: subj: Public Opinion Algorithms China ab: At present, China is in the period of social transformation, and social contradictions are gradually prominent. The research on NPO (network public opinion) emergency warning methods is gradually increasing. Some existing laws and regulations are abstracted and principled in content, lacking specific implementation rules and corresponding supporting measures, especially the legal rules of emergency administrative procedures. Therefore, the legal early warning model of NPO public crisis is based on emotional dimension content, NPO emotional characteristics, emotional dimension elements, and machine learning classification algorithm to construct text ET (emotional tendencies) classifier, which can be used to make ET judgment on text data. The results show that after PSO (particle swarm optimization) algorithm optimization, the precision, recall rate, and micro-average are significantly improved, and the precision is increased by nearly 14% and 80%. The conclusion shows that using PSO optimization parameters improves the classification effect of the classifier, and a better NPO crisis early warning model can be obtained. pubtype: Academic Journal doctype: algorithm equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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