基于相对信息熵和语言加权算子的网络舆情突发事件模糊多指标应急决策方法  

Fuzzy Multi-indicator Group Decision Making of Internet Public Opinion Emergency Based on Relative Entropy and Linguistic Weighted Operator

作　　者：张倩生 冯雨桐 Zhang Qiansheng;Feng Yutong(College of Finance,Guangdong University of Foreign Studies,Guangzhou,510006,China)

机构地区：[1]广东外语外贸大学金融学院,广州510006

出　　处：《数据采集与处理》2018年第1期171-178,共8页Journal of Data Acquisition and Processing

基　　金：广东省自然科学基金(2017A030313435)资助项目;广东省高等教育"创新强校工程"(GWTP-GC-2014-03)资助项目;国家社会科学基金(13CGL130)资助项目

摘　　要：在仔细分析网络舆情突发事件的成因与应急决策特性的基础上,从舆情状态、突发事件本身和民众关注3方面构建网络舆情突发事件的属性指标。考虑到网络舆情突发事件指标的语言不确定性和多样性,以及应急专家对舆情事件危机评价的语言判断矩阵差异性,本文提出一个基于相对信息熵和语言加权算子的网络舆情突发事件模糊多指标群决策模型。首先根据应急指标重要性的判断矩阵,计算出每个专家对各指标权重的评价;然后基于相对熵的多属性组合赋权方法来获取应急专家的权重,计算得出各舆情事件指标的综合权重;再根据各应急专家对网络舆情事件的语言指标评价值,利用语言加权算术平均算子,集结得到各网络舆情突发事件的综合决策评价值,进而对各舆情事件危机程度快速排序。最后,通过一个实例来证明本文方法的实用性。On the basis of analyzing causes of Internet public opinion emergency(IPOE)and features of emergency decision,the indicators of IPOE are constructed from three aspects of the state of Internet public opinion,the emergency itself and the public attention.Due to the uncertainty and diversity of indicators for IPOE and the difference of linguistic judgment matrices on crisis evaluation from experts,we propose a fuzzy multi indicator group decision making of IPOE based on relative entropy and linguistic weighted operator.Firstly,according to judgment matrix of evaluating importance of indicators,we calculate the weights of indicators of each expert,and then use the multi indicator combination weighting method based on relative entropy to get the experts′weights,so we can calculate comprehensive weights of indicators of IPOE.Secondly,based on linguistic indicator evaluation of IPOE from experts,we use the linguistic weighted arithmetic average operator to gain the comprehensive evaluation of the value of each IPOE,and quickly sort out IPOEs according to crisis levels.Finally,an example is carried out to show the practicality of the proposed method.

关 键 词：网络舆情突发事件 应急群决策 语言判断矩阵 相对熵 语言加权算子 

分 类 号：TP391[自动化与计算机技术—计算机应用技术] C931[自动化与计算机技术—计算机科学与技术]