基于灰色系统理论的网络舆情预测与分级方法研究
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摘要
[目的/意义]建立一套行之有效的网络舆情预测和分级管理系统对提高政府网络监管效率具有重大意义。[方法/过程]分时段记录网络舆情事件的新浪微指数、百度指数、头条指数作为事件热度的衡量指标,运用EGM(1.1)模型对舆情事件的发展趋势进行预测,并在预测数据基础上,运用灰色关联分析方法,提出网络舆情事件分级方案。综合运用上述两模型确立的舆情监管体系对"江歌案宣判""上海携程亲子园虐童案""莫焕晶案二审宣判"等事件进行分析评价。[结果/结论]模型在处理舆情管理问题方面具有良好的适用性和精确性。结合舆情事件特点与分析结果,向政府及公司危机公关部门等提出了相应建议。
[Purpose/significance]Establishing an effective system to predict and grade network public opinion has great significance in improving the efficiency of governmental network supervision.[Method/process]First,record Sina micro index,Baidu index,Toutiao index of network public opinion events hour by hour which will be used as the measurement indices of event concern degree.Then EGM(1.1) Model is used to predict the development trend of public opinion events and grey relational analysis is applied to propose a grading scheme for network public opinion events based on the predicted data.The paper analyzes and evaluates typical events such as the"Jiang Ge case""Shanghai Xiecheng child abusing case""Mo Huanjing case"with the public opinion supervision system established by the two models above.[Result/conclusion]Established models have great applicability and precision in dealing with the issues of public opinion management.Combined with the characteristics of public opinion events and the system analysis results,suggestions oriented the government and the company's public relation departments have been made in the paper.
[Purpose/significance]Establishing an effective system to predict and grade network public opinion has great significance in improving the efficiency of governmental network supervision.[Method/process]First,record Sina micro index,Baidu index,Toutiao index of network public opinion events hour by hour which will be used as the measurement indices of event concern degree.Then EGM(1.1) Model is used to predict the development trend of public opinion events and grey relational analysis is applied to propose a grading scheme for network public opinion events based on the predicted data.The paper analyzes and evaluates typical events such as the"Jiang Ge case""Shanghai Xiecheng child abusing case""Mo Huanjing case"with the public opinion supervision system established by the two models above.[Result/conclusion]Established models have great applicability and precision in dealing with the issues of public opinion management.Combined with the characteristics of public opinion events and the system analysis results,suggestions oriented the government and the company's public relation departments have been made in the paper.
引文
[1]张艳丰,李贺,彭丽徽,等.基于语义隶属度模糊推理的网络舆情监测预警实证研究[J].情报理论与实践,2017,40(9):82-89.
[2]刘思峰,党耀国,方志耕,等.灰色系统理论及其应用[M].北京:科学出版社,2005:13.
[3]李文杰,化存才,何伟全,等.网络舆情信息的综合评价指标体系构建与模糊评判模型[J].情报科学,2015(9):93-99.
[4]刘健,毕强,李瑞.微博舆情信息传播效果评价指标体系构建研究---基于模糊数据包络分析法[J].情报理论与实践,2016,39(12):31-38.
[5]王高飞,李明.基于AHP-模糊综合分析的移动社交网络舆情预警模型研究[J].现代情报,2017,37(1):41-44.
[6]刘毅.基于三角模糊数的网络舆情预警指标体系构建[J].统计与决策,2012(2):12-15.
[7]梁晓贺,田儒雅,吴蕾,等.基于超网络的微博舆情主题挖掘方法[J].情报理论与实践,2017,40(10):100-105.
[8]田世海,吕德丽.改进潜在语义分析和支持向量机算法用于突发安全事件舆情预警[J].数据分析与知识发现,2017,1(2):11-18.
[9]郭韧,李红,陈福集.基于可拓聚类的网络舆情演化预测研究[J].情报理论与实践,2017(1):83-87.
[10]张一文,齐佳音,方滨兴,等.基于贝叶斯网络建模的非常规危机事件网络舆情预警研究[J].图书情报工作,2012,56(2):76-81.
[11]胡悦,王亚民.基于模糊神经网络的微博舆情趋势预测方法[J].情报科学,2017,35(12):28-33.
[12]何建民,李雪.面向微博舆情演化分析的隐马尔科夫模型研究[J].情报科学,2016,34(4):7-12.
[13]YANG H L,LIN Q F.Opinion mining for multiple types of emotion-embedded products/services through evolutionary strategy[J].Expert Systems with Applications,2018,99:44-55.
[14]安璐,吴林.融合主题与情感特征的突发事件微博舆情演化分析[J].图书情报工作,2017(15):120-129.
[15]赵晓航.基于情感分析与主题分析的“后微博”时代突发事件政府信息公开研究---以新浪微博“天津爆炸”话题为例[J].图书情报工作,2016,60(20):104-111.
[16]董坚峰.基于Web挖掘的突发事件网络舆情预警研究[J].现代情报,2014,34(2):43-47.
[17]李彤,宋之杰.基于模型集成的突发事件舆情分析与趋势预测研究[J].系统工程理论与实践,2015,35(10):2582-2587.
[18]王英,龚花萍.基于情感维度的大数据网络舆情情感倾向性分析研究---以“南昌大学自主保洁”微博舆情事件为例[J].情报科学,2017(4):37-42.
[19]GOPALAKRISHNAN V,RAMASWAMY C.Patient opinion mining to analyze drugs satisfaction using supervised learning[J].Journal of Applied Research&Technology,2017,15(4):311-319.
[20]CHERIF W,MADANI A,KISSI M.Towards an efficient opinion measurement in Arabic comments[J].Procedia Computer Science,2015,73:122-129.
[21]陈涛,林杰.基于搜索引擎关注度的网络舆情时空演化比较分析---以谷歌趋势和百度指数比较为例[J].情报杂志,2013(3):7-10.
[22]王康,李含伟.自媒体时代的企业网络舆情应对策略研究---基于上市公司百度指数的研究[J].情报科学,2018,36(1):113-117.
[23]张玉强.基于微指数变化曲线的阶段性事件识别及演化关系分析[D].青岛:山东科技大学,2017.
[24]刘思峰,杨英杰.灰色系统研究进展(2004-2014)[J].南京航空航天大学学报,2015,47(1):1-18.
[25]钱吴永,党耀国.基于振荡序列的GM(1,1)模型[J].系统工程理论与实践,2009,29(3):151-156.
[26]刘思峰,谢乃明,等.基于相似性和接近性视角的新型灰色关联分析模型[J].系统工程理论与实践,2010,30(5):881-887.
[2]刘思峰,党耀国,方志耕,等.灰色系统理论及其应用[M].北京:科学出版社,2005:13.
[3]李文杰,化存才,何伟全,等.网络舆情信息的综合评价指标体系构建与模糊评判模型[J].情报科学,2015(9):93-99.
[4]刘健,毕强,李瑞.微博舆情信息传播效果评价指标体系构建研究---基于模糊数据包络分析法[J].情报理论与实践,2016,39(12):31-38.
[5]王高飞,李明.基于AHP-模糊综合分析的移动社交网络舆情预警模型研究[J].现代情报,2017,37(1):41-44.
[6]刘毅.基于三角模糊数的网络舆情预警指标体系构建[J].统计与决策,2012(2):12-15.
[7]梁晓贺,田儒雅,吴蕾,等.基于超网络的微博舆情主题挖掘方法[J].情报理论与实践,2017,40(10):100-105.
[8]田世海,吕德丽.改进潜在语义分析和支持向量机算法用于突发安全事件舆情预警[J].数据分析与知识发现,2017,1(2):11-18.
[9]郭韧,李红,陈福集.基于可拓聚类的网络舆情演化预测研究[J].情报理论与实践,2017(1):83-87.
[10]张一文,齐佳音,方滨兴,等.基于贝叶斯网络建模的非常规危机事件网络舆情预警研究[J].图书情报工作,2012,56(2):76-81.
[11]胡悦,王亚民.基于模糊神经网络的微博舆情趋势预测方法[J].情报科学,2017,35(12):28-33.
[12]何建民,李雪.面向微博舆情演化分析的隐马尔科夫模型研究[J].情报科学,2016,34(4):7-12.
[13]YANG H L,LIN Q F.Opinion mining for multiple types of emotion-embedded products/services through evolutionary strategy[J].Expert Systems with Applications,2018,99:44-55.
[14]安璐,吴林.融合主题与情感特征的突发事件微博舆情演化分析[J].图书情报工作,2017(15):120-129.
[15]赵晓航.基于情感分析与主题分析的“后微博”时代突发事件政府信息公开研究---以新浪微博“天津爆炸”话题为例[J].图书情报工作,2016,60(20):104-111.
[16]董坚峰.基于Web挖掘的突发事件网络舆情预警研究[J].现代情报,2014,34(2):43-47.
[17]李彤,宋之杰.基于模型集成的突发事件舆情分析与趋势预测研究[J].系统工程理论与实践,2015,35(10):2582-2587.
[18]王英,龚花萍.基于情感维度的大数据网络舆情情感倾向性分析研究---以“南昌大学自主保洁”微博舆情事件为例[J].情报科学,2017(4):37-42.
[19]GOPALAKRISHNAN V,RAMASWAMY C.Patient opinion mining to analyze drugs satisfaction using supervised learning[J].Journal of Applied Research&Technology,2017,15(4):311-319.
[20]CHERIF W,MADANI A,KISSI M.Towards an efficient opinion measurement in Arabic comments[J].Procedia Computer Science,2015,73:122-129.
[21]陈涛,林杰.基于搜索引擎关注度的网络舆情时空演化比较分析---以谷歌趋势和百度指数比较为例[J].情报杂志,2013(3):7-10.
[22]王康,李含伟.自媒体时代的企业网络舆情应对策略研究---基于上市公司百度指数的研究[J].情报科学,2018,36(1):113-117.
[23]张玉强.基于微指数变化曲线的阶段性事件识别及演化关系分析[D].青岛:山东科技大学,2017.
[24]刘思峰,杨英杰.灰色系统研究进展(2004-2014)[J].南京航空航天大学学报,2015,47(1):1-18.
[25]钱吴永,党耀国.基于振荡序列的GM(1,1)模型[J].系统工程理论与实践,2009,29(3):151-156.
[26]刘思峰,谢乃明,等.基于相似性和接近性视角的新型灰色关联分析模型[J].系统工程理论与实践,2010,30(5):881-887.
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