Intelligent modeling and simulation of online public opinion for major accidents based on proactive safety
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.
proactive safetymajor accidentonline public opinion analysissusceptible-exposed-infectious-removed(SEIR)modelsentiment analysis
NETL
NSTL
万方数据
