基于概率语言术语集的突发事件网络舆情应急决策方法

doi:10.16265/j.cnki.issn1003-3033.2025.10.1691

摘要/Abstract

摘要： 为解决突发事件网络舆情应急响应决策中舆情信息的不确定性和非精确性等问题,提出一种基于概率语言术语集(PLTSs)的突发事件网络舆情应急决策方法。首先,利用Python编程和机器学习等方法对网络舆情信息进行爬取、预处理和情感倾向分析,得到表征为PLTSs术语的决策矩阵;其次,基于指标相关性权重赋权法(CRITIC)客观确定各属性权重;然后,基于灰色关联分析(GRA)模型求得各案例的综合关联度并进行排序;最后,通过台风灾害案例验证所提方法的有效性和实用性。结果表明:所提方法能实时监测突发事件网络舆情,客观智能获取决策数据,从而实现对台风灾害的量化评估,为相关应急部门有效应对突发事件网络舆情提供良好的决策支持。

关键词: 概率语言术语集(PLTSs), 突发事件, 网络舆情, 应急决策, 指标相关性权重赋权法(CRITIC)

Abstract:

Aiming at the problems of uncertainty and imprecision of public opinion information in emergency response decision-making of emergency events network public opinion, a decision-making method for emergency response to online public opinion on emergency events based on PLTSs was proposed. Firstly, methods such as Python programming and machine learning were used to crawl, preprocess and analyze the sentiment tendency of online public opinion information to obtain the decision matrix characterized by PLTSs. Secondly, the CRITIC assignment method was used to objectively determine the weights of each attribute. Then, the comprehensive relevance of each case was obtained and ranked based on grey correlation analysis(GRA) model. Finally, the effectiveness and practicability of the proposed method were verified through typhoon disaster cases. The results of the study show that the proposed method is able to monitor the online public opinion of emergencies in real time, obtain decision-making data objectively and intelligently so as to realize the quantitative assessment of typhoon disasters. It provides good decision-making support for the relevant emergency departments to effectively respond to the online public opinion of emergencies.

Key words: probabilistic linguistic term sets(PLTSs), emergencies, online public opinion, emergency decision-making, criteria importance through intercriteria correlatio(CRITIC)

中图分类号:

X913.4安全系统工程

陈冲, 谭睿璞, 张文德. 基于概率语言术语集的突发事件网络舆情应急决策方法[J]. 中国安全科学学报, 2025, 35(10): 230-238.

CHEN Chong, TAN Ruipu, ZHANG Wende. Research on emergency decision-making method for online public opinion on emergency events based on probabilistic linguistic term sets[J]. China Safety Science Journal, 2025, 35(10): 230-238.

图/表 10

表1

图1

图2

表2

表征为PLTSs的评估决策矩阵

方案	c₁	c₂	c₃	c₄
$Y 1$	${(s 0 (0.194), s 1 (0.182), s 2 (0.197), s 3 (0.103), s 4 (0.323)}$	${(s 0 (0.462), s 1 (0.151), s 2 (0.151), s 3 (0.087), s 4 (0.149)}$	${(s 0 (0.481), s 1 (0.133), s 2 (0.082), s 3 (0.068), s 4 (0.235)}$	${(s 0 (0.341), s 1 (0.259), s 2 (0.156), s 3 (0.115), s 4 (0.129)}$
$Y 2$	${(s 0 (0.192), s 1 (0.198), s 2 (0.209), s 3 (0.095), s 4 (0.305)}$	${(s 0 (0.418), s 1 (0.219), s 2 (0.166), s 3 (0.112), s 4 (0.086)}$	${(s 0 (0.586), s 1 (0.143), s 2 (0.090), s 3 (0.074), s 4 (0.107)}$	${(s 0 (0.362), s 1 (0.320), s 2 (0.196), s 3 (0.081), s 4 (0.040)}$
$Y 3$	${(s 0 (0.193), s 1 (0.162), s 2 (0.181), s 3 (0.113), s 4 (0.351)}$	${(s 0 (0.451), s 1 (0.189), s 2 (0.142), s 3 (0.085), s 4 (0.132)}$	${(s 0 (0.464), s 1 (0.140), s 2 (0.109), s 3 (0.091), s 4 (0.195)}$	${(s 0 (0.427), s 1 (0.226), s 2 (0.136), s 3 (0.111), s 4 (0.099)}$
$Y 4$	${(s 0 (0.179), s 1 (0.197), s 2 (0.202), s 3 (0.111), s 4 (0.310)}$	${(s 0 (0.421), s 1 (0.170), s 2 (0.170), s 3 (0.134), s 4 (0.105)}$	${(s 0 (0.649), s 1 (0.117), s 2 (0.074), s 3 (0.058), s 4 (0.102)}$	${(s 0 (0.333), s 1 (0.334), s 2 (0.189), s 3 (0.090), s 4 (0.054)}$
$Y 5$	${(s 0 (0.193), s 1 (0.186), s 2 (0.185), s 3 (0.103), s 4 (0.333)}$	${(s 0 (0.495), s 1 (0.195), s 2 (0.155), s 3 (0.090), s 4 (0.065)}$	${(s 0 (0.619), s 1 (0.120), s 2 (0.093), s 3 (0.072), s 4 (0.095)}$	${(s 0 (0.468), s 1 (0.222), s 2 (0.165), s 3 (0.091), s 4 (0.055)}$
$Y 6$	${(s 0 (0.215), s 1 (0.201), s 2 (0.219), s 3 (0.110), s 4 (0.255)}$	${(s 0 (0.425), s 1 (0.202), s 2 (0.171), s 3 (0.115), s 4 (0.087)}$	${(s 0 (0.523), s 1 (0.156), s 2 (0.112), s 3 (0.088), s 4 (0.121)}$	${(s 0 (0.434), s 1 (0.243), s 2 (0.188), s 3 (0.086), s 4 (0.048)}$
$Y 7$	${(s 0 (0.207), s 1 (0.164), s 2 (0.186), s 3 (0.124), s 4 (0.320)}$	${(s 0 (0.446), s 1 (0.169), s 2 (0.161), s 3 (0.120), s 4 (0.104)}$	${(s 0 (0.583), s 1 (0.128), s 2 (0.091), s 3 (0.073), s 4 (0.125)}$	${(s 0 (0.496), s 1 (0.223), s 2 (0.134), s 3 (0.078), s 4 (0.069)}$
$Y 8$	${(s 0 (0.187), s 1 (0.172), s 2 (0.189), s 3 (0.118), s 4 (0.334)}$	${(s 0 (0.521), s 1 (0.129), s 2 (0.156), s 3 (0.087), s 4 (0.106)}$	${(s 0 (0.506), s 1 (0.071), s 2 (0.146), s 3 (0.112), s 4 (0.165)}$	${(s 0 (0.329), s 1 (0.225), s 2 (0.174), s 3 (0.140), s 4 (0.132)}$
$Y 9$	${(s 0 (0.200), s 1 (0.175), s 2 (0.202), s 3 (0.103), s 4 (0.321)}$	${(s 0 (0.472), s 1 (0.179), s 2 (0.116), s 3 (0.106), s 4 (0.126)}$	${(s 0 (0.545), s 1 (0.129), s 2 (0.091), s 3 (0.069), s 4 (0.165)}$	${(s 0 (0.455), s 1 (0.211), s 2 (0.151), s 3 (0.078), s 4 (0.105)}$
$Y 10$	${(s 0 (0.195), s 1 (0.203), s 2 (0.225), s 3 (0.094), s 4 (0.283)}$	${(s 0 (0.399), s 1 (0.217), s 2 (0.178), s 3 (0.101), s 4 (0.105)}$	${(s 0 (0.643), s 1 (0.146), s 2 (0.086), s 3 (0.052), s 4 (0.073)}$	${(s 0 (0.311), s 1 (0.341), s 2 (0.207), s 3 (0.090), s 4 (0.050)}$
$Y 11$	${(s 0 (0.211), s 1 (0.185), s 2 (0.193), s 3 (0.099), s 4 (0.311)}$	${(s 0 (0.441), s 1 (0.196), s 2 (0.151), s 3 (0.078), s 4 (0.134)}$	${(s 0 (0.447), s 1 (0.153), s 2 (0.153), s 3 (0.071), s 4 (0.176)}$	${(s 0 (0.365), s 1 (0.250), s 2 (0.196), s 3 (0.095), s 4 (0.095)}$
$Y 12$	${(s 0 (0.213), s 1 (0.174), s 2 (0.186), s 3 (0.117), s 4 (0.309)}$	${(s 0 (0.430), s 1 (0.160), s 2 (0.210), s 3 (0.100), s 4 (0.100)}$	${(s 0 (0.504), s 1 (0.120), s 2 (0.064), s 3 (0.112), s 4 (0.200)}$	${(s 0 (0.353), s 1 (0.245), s 2 (0.216), s 3 (0.078), s 4 (0.108)}$
$Y 13$	${(s 0 (0.211), s 1 (0.172), s 2 (0.189), s 3 (0.100), s 4 (0.327)}$	${(s 0 (0.444), s 1 (0.186), s 2 (0.157), s 3 (0.095), s 4 (0.118)}$	${(s 0 (0.539), s 1 (0.099), s 2 (0.130), s 3 (0.042), s 4 (0.190)}$	${(s 0 (0.415), s 1 (0.207), s 2 (0.175), s 3 (0.087), s 4 (0.116)}$
$Y 14$	${(s 0 (0.178), s 1 (0.150), s 2 (0.430), s 3 (0.084), s 4 (0.159)}$	${(s 0 (0.357), s 1 (0.107), s 2 (0.321), s 3 (0.179), s 4 (0.036)}$	${(s 0 (0.429), s 1 (0.000), s 2 (0.429), s 3 (0.143), s 4 (0.000)}$	${(s 0 (0.346), s 1 (0.154), s 2 (0.192), s 3 (0.269), s 4 (0.038)}$
$Y 15$	${(s 0 (0.203), s 1 (0.201), s 2 (0.203), s 3 (0.103), s 4 (0.290)}$	${(s 0 (0.486), s 1 (0.201), s 2 (0.116), s 3 (0.086), s 4 (0.112)}$	${(s 0 (0.497), s 1 (0.116), s 2 (0.112), s 3 (0.076), s 4 (0.198)}$	${(s 0 (0.419), s 1 (0.204), s 2 (0.147), s 3 (0.114), s 4 (0.116)}$

表2

图3

表3

灰色关联系数矩阵

方案	ξ⁺				ξ^-				$G i$
方案	$c 1$	$c 2$	$c 3$	$c 4$	$c 1$	$c 2$	$c 3$	$c 4$	$G i$
$Y 1$	0.892	0.930	0.712	0.890	0.478	0.634	0.416	0.680	0.656
$Y 2$	0.889	0.925	0.637	0.745	0.497	0.796	0.632	0.869	0.451
$Y 3$	0.875	0.912	0.744	0.852	0.447	0.679	0.461	0.794	0.623
$Y 4$	0.889	1.000	0.615	0.780	0.491	0.721	0.657	0.850	0.470
$Y 5$	0.876	0.839	0.625	0.769	0.469	0.921	0.667	1.000	0.398
$Y 6$	0.816	0.939	0.674	0.760	0.557	0.792	0.588	0.952	0.444
$Y 7$	0.872	0.963	0.656	0.755	0.484	0.745	0.592	0.964	0.458
$Y 8$	0.887	0.903	0.765	0.959	0.465	0.769	0.500	0.656	0.630
$Y 9$	0.897	0.899	0.692	0.788	0.479	0.695	0.514	0.791	0.521
$Y 10$	0.876	0.964	0.590	0.775	0.518	0.738	0.736	0.824	0.413
$Y 11$	0.872	0.914	0.770	0.831	0.495	0.671	0.485	0.788	0.590
$Y 12$	0.861	0.993	0.700	0.805	0.500	0.737	0.453	0.733	0.606
$Y 13$	0.880	0.939	0.731	0.817	0.475	0.717	0.474	0.740	0.560
$Y 14$	0.683	0.907	0.602	0.953	0.445	0.603	0.470	0.560	0.581
$Y 15$	0.859	0.859	0.741	0.869	0.519	0.748	0.461	0.733	0.551

表3

表4

图4

图5

表5

不同排序方法对比分析结果

方案	传统 TOPSIS 方法 $P i$	排序	PLWA 算子方法 $Q i$	排序	文中方法 $G i$	排序
$Y 1$	0.609	1	0.314	2	0.607	1
$Y 2$	0.526	12	0.274	12	0.465	12
$Y 3$	0.587	4	0.304	6	0.582	3
$Y 4$	0.536	10	0.279	10	0.476	10
$Y 5$	0.499	15	0.260	15	0.429	15
$Y 6$	0.522	13	0.276	11	0.464	13
$Y 7$	0.532	11	0.274	13	0.471	11
$Y 8$	0.597	3	0.313	3	0.590	2
$Y 9$	0.567	9	0.285	9	0.514	9
$Y 10$	0.519	14	0.271	14	0.441	14
$Y 11$	0.582	6	0.304	5	0.563	5
$Y 12$	0.581	7	0.308	4	0.573	4
$Y 13$	0.584	5	0.298	7	0.542	7
$Y 14$	0.599	2	0.341	1	0.559	6
$Y 15$	0.579	8	0.291	8	0.537	8

表5

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属性	指标差异性	指标冲突性	信息量	属性权重/%
C₁	0.012	3.514	0.042	22.762 7
C₂	0.012	3.074	0.036	19.483 8
C₃	0.028	2.218	0.062	34.008 2
C₄	0.020	2.206	0.044	23.745 2