基于RF算法的突发事件网络舆情演化预测分析
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摘要
【目的/意义】突发事件类网络舆情演化情况的掌握对舆情监管部门而言至关重要。鉴于此,本文致力于构建能够准确预测舆情演化的模型,此预测模型的建立可拓宽舆情预测的渠道,为舆情参与主体和监管部门及时掌握舆情演化态势提供方法依据。【方法/过程】基于随机森林(RF)算法建立突发事件网络舆情演化预测模型,以微博和第三方舆情监测平台作为变量数据来源,以R语言为操作环境,然后进行模型的训练与预测。【结果/结论】实验表明,较之其它方法,本文构建的模型有更高的拟合度和更低的误差值。从结果来看,本模型的预测输出值与真实值最为接近,较好地实现了对舆情演化的预测,将RF算法应用在舆情预测的研究中具有一定的先进性。
【Purpose/significance】It is very important for public opinion supervision department to master the evolution of public opinion in emergency network. In view of this, this paper is committed to building a model that can accurately predict the evolution of public opinion. The establishment of this prediction model broadens the channels of public opinion prediction, and provides a method basis for the participants and regulatory departments to grasp the evolution situation of public opinion in time.【Method/process】Based on stochastic forest(RF) algorithm, a prediction model of public opinion evolution in emergency network is established. Weibo and third-party public opinion monitoring platform are used as variable data sources, R language is used as operating environment, and then the model is trained and predicted.【Result/conclusion】Experiments show that the proposed model has higher fitting degree and lower error value than other methods. From the results, the predicted output value of this model is the closest to the real value, and the prediction of public opinion evolution has been well completed. The application of RF algorithm in the study of public opinion prediction has certain advancement.
【Purpose/significance】It is very important for public opinion supervision department to master the evolution of public opinion in emergency network. In view of this, this paper is committed to building a model that can accurately predict the evolution of public opinion. The establishment of this prediction model broadens the channels of public opinion prediction, and provides a method basis for the participants and regulatory departments to grasp the evolution situation of public opinion in time.【Method/process】Based on stochastic forest(RF) algorithm, a prediction model of public opinion evolution in emergency network is established. Weibo and third-party public opinion monitoring platform are used as variable data sources, R language is used as operating environment, and then the model is trained and predicted.【Result/conclusion】Experiments show that the proposed model has higher fitting degree and lower error value than other methods. From the results, the predicted output value of this model is the closest to the real value, and the prediction of public opinion evolution has been well completed. The application of RF algorithm in the study of public opinion prediction has certain advancement.
引文
1周耀明,张慧成,王波.网络舆情演化模式分析[J].信息工程大学学报,2012,13(3):334-341.
2 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,2017,61(15):120-129.
3 陈福集,张燕.基于E-Divisive的网络舆情演化分析[J].情报杂志,2016,35(4):75-79.
4 陈福集,黄江玲.基于演化博弈的网络舆情传播的羊群效应研究[J].情报杂志,2013,32(10):1-5.
5 马宁,刘怡君.基于超网络的舆情演化多主体建模[J].系统管理学报,2015,24(6):785-794,805.
6 曹学艳,宋彦宁,刘海涛,李仕明.基于最小二乘法的突发事件网络舆情演化规律研究[J].图书情报工作,2013,57(24):101-105.
7 宋姜,吴鹏,甘利人.网民沉默因素的元胞自动机舆情演化建模及仿真[J].情报理论与实践,2015,38(8):124-129.
8 陈涛,林杰.基于模糊元胞自动机的网络舆情演化模型[J].情报学报,2013,32(9):920-928.
9 马永军,杜禹阳.基于复杂网络Deffuant模型的舆情演化规律研究[J].情报杂志,2018,37(6):91-95,159.
10 孙靖超,周睿,李培岳,芦天亮.基于循环神经网络的网络舆情趋势预测研究[J].情报科学,2018,36(8):118-122,127.
11 曾子明,黄城莺.基于BP神经网络的突发传染病舆情热度趋势预测模型研究[J].现代情报,2018,38(5):37-44,52.
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 陈福集,黄江玲.三方博弈视角下的网络舆情演化研究[J].情报科学,2015,33(9):22-26.
14 陈福集,黄江玲.我国网络舆情演变文献研究综述[J].情报杂志,2013,32(7):54-58,92.
15 宋宁,刘婵君.过程与要素视角下的突发事件网络舆情演化规律研究综述[J].图书情报工作,2016,60(15):140-147.
16 于兆吉,张嘉桐.基于扎根理论的突发性网络舆情演化影响因素研究[J].东北大学学报(社会科学版),2016,18(5):498-502,509.
2 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,2017,61(15):120-129.
3 陈福集,张燕.基于E-Divisive的网络舆情演化分析[J].情报杂志,2016,35(4):75-79.
4 陈福集,黄江玲.基于演化博弈的网络舆情传播的羊群效应研究[J].情报杂志,2013,32(10):1-5.
5 马宁,刘怡君.基于超网络的舆情演化多主体建模[J].系统管理学报,2015,24(6):785-794,805.
6 曹学艳,宋彦宁,刘海涛,李仕明.基于最小二乘法的突发事件网络舆情演化规律研究[J].图书情报工作,2013,57(24):101-105.
7 宋姜,吴鹏,甘利人.网民沉默因素的元胞自动机舆情演化建模及仿真[J].情报理论与实践,2015,38(8):124-129.
8 陈涛,林杰.基于模糊元胞自动机的网络舆情演化模型[J].情报学报,2013,32(9):920-928.
9 马永军,杜禹阳.基于复杂网络Deffuant模型的舆情演化规律研究[J].情报杂志,2018,37(6):91-95,159.
10 孙靖超,周睿,李培岳,芦天亮.基于循环神经网络的网络舆情趋势预测研究[J].情报科学,2018,36(8):118-122,127.
11 曾子明,黄城莺.基于BP神经网络的突发传染病舆情热度趋势预测模型研究[J].现代情报,2018,38(5):37-44,52.
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 陈福集,黄江玲.三方博弈视角下的网络舆情演化研究[J].情报科学,2015,33(9):22-26.
14 陈福集,黄江玲.我国网络舆情演变文献研究综述[J].情报杂志,2013,32(7):54-58,92.
15 宋宁,刘婵君.过程与要素视角下的突发事件网络舆情演化规律研究综述[J].图书情报工作,2016,60(15):140-147.
16 于兆吉,张嘉桐.基于扎根理论的突发性网络舆情演化影响因素研究[J].东北大学学报(社会科学版),2016,18(5):498-502,509.