摘要
从宏观角度研究基于关键词的网络舆情热度有助于相关机构把握目标群体的整体舆情动态,从而实现精准施策,提升舆论引导水平.本文以新浪微博数据为例,采用因子分析方法 (Factor Analysis, FA),挖掘舆情热度内在影响因素,并通过改进Elman网络结构,利用遗传算法(Genetic Algorithm, GA)优化初始参数来构建模型对网络舆情关键词热度进行分析预测.实验结果表明,所提出的方法相较于采用原始数据集和标准Elman网络的预测结果,具有更高的预测精度,可为相关研究提供决策支持.
It is helpful for institutions to master the whole trends of target group that research on keywords popularity of network public opinions from a macroscopic perspective, precisely formulating corresponding strategies to enhance the level of opinion guidance. With Sina Weibo data set as an example, Factor Analysis(FA) is used to mine the internal factors of public opinions;a model that analyzes and predicts the keyword popularity of network public opinions is created through the initial parameters optimized by Genetic Algorithm(GA) and Elman network structure. The results show that predictions made by our method is more precise than those of original data sets and standard Elman network.Thus, it can be applied to providing reference for decision making.
作者
肖光华
王清莲
XIAO Guang-Hua;WANG Qing-Lian(Department of Equipment Engineering,Jiangsu Urban and Rural Construction College,Changzhou 213147,China;College of Computer and Information Engineering,Hohai University,Nanjing 210098,China;Lifelong Education Research Center,Changzhou Open University,Changzhou 213001,China)
出处
《计算机系统应用》
2021年第3期243-249,共7页
Computer Systems & Applications
基金
全国教育信息技术研究重点课题(183220001)。
关键词
因子分析
ELMAN
遗传算法
网络舆情
关键词热度
factor analysis
Elman
genetic algorithm
network public opinion
keyword popularity
作者简介
通讯作者:肖光华,E-mail:smoothxiao@163.com。
