基于主动安全的重大事故网络舆情智能建模与仿真
Intelligent modeling and simulation of online public opinion for major accidents based on proactive safety
陈鑫 1谢科范1
作者信息
- 1. 武汉理工大学 管理学院,湖北 武汉 430070
- 折叠
摘要
针对重大安全事故发生时,肆意传播且真伪难辨的信息易引起社会负面情绪,给应急处置及救援工作带来不便的问题,基于主动安全理念,结合情感分析模型、主题计算模型和易感人群-潜伏人群-感染人群-离去人群(SEIR)模型,开展重大安全事故网络舆情仿真及控制策略研究.运用卷积深度置信网络(CDBN)、时域卷积网络(TCN)、条件随机场(CRF)组成的CDBN-TCN-CRF情感分析模型及T分布瓦瑟斯坦自编码器(TWAE)主题计算模型,识别情感极性及主题类别,跟踪网络舆情情感倾向及民众关注热点;运用SEIR模型来预测网络舆情走势,并研究网络舆情的传播过程和影响因素.结果表明:CDBN-TCN-CRF情感分析模型、TWAE主题计算模型及SEIR模型结合使用,可以更好地展现其对网络舆情深度分析与趋势预测的能力.
Abstract
In case of major security accidents,information disseminated wantonly and difficult to discern in terms of authenticity can easily cause negative social sentiments.This poses several issues to emergency rescue.Sentiment analysis,topic calculation,and the SEIR model were used to investigate online opinion simulation and control strategies for major security accidents.The CDBN-TCN-CRF sentiment analysis model was proposed by coupling the Convolutional Deep Belief Networks(CDBN),temporal convolutional networks(TCN),and conditional random fields(CRF).Then,the T-distributed Wasserstein autoencoder(TWAE)topic computation model was used to discern sentiment polarity,topic categories,race sentiment trajectory,and public attention focal points within the network discourse.Furthermore,the proposed SEIR model was used to predict online public opinion tendency and analyze the dissemination dynamics and their influencing factors.The results indicate that the coupling of CDBN-TCN-CRF sentiment analysis,TWAE topic computation,and SEIR model has a better prediction performance on network discourse analysis and trend analysis.
关键词
主动安全/重大事故/网络舆情分析/易感人群-潜伏人群-感染人群-离去人群(SEIR)模型/情感分析Key words
proactive safety/major accident/online public opinion analysis/susceptible-exposed-infectious-removed(SEIR)model/sentiment analysis引用本文复制引用
出版年
2024
NETL
NSTL
万方数据