Research On Microblog Public Opinion Portrait Model Of Major Epidemic Situation Based On Multi Dimensional Feature Fusion

Since the outbreak of the COVID-19,the views,opinions and attitudes related to the development trend of the epidemic and the situation of prevention and control have intersected and collided in the microblog platform,forming a microblog public opinion field of major epidemic with rapid changes and complex risks.The COVID-19 lasts for a long time and has a wide range of impacts.The related microblog public opinion management is not only a serious challenge to the network information ecological governance,but also a necessary work to maintain the smooth implementation of the epidemic prevention and control work,which is of great significance to social stability and national security during the epidemic.Based on this,this research combines the user portrait theory with the public opinion supervision demand of major epidemic,takes the COVID-19 epidemic as the background,and comprehensively depicts the public opinion portrait of major epidemic microblog by reasonably integrating the multidimensional characteristics of online public opinion,and conducts research on the public opinion portrait model of major epidemic microblog from the aspects of tag system design,feature tag extraction,multi-dimensional feature fusion,portrait model construction,and application value analysis,Make contributions to the establishment and improvement of the supervision and early warning mechanism of public opinion on microblog for major epidemic.First of all,a CNN-Bi GRU text emotion classification model based on convolutional neural network and two-way gated loop unit is proposed.The emotional distribution of netizens in microblog public opinion of major epidemic has an important role in promoting the evolution of public opinion.Analyzing the emotional characteristics of netizens is an important work of public opinion management.Crawl the epidemic related blog data from the microblog platform,build the experimental data set,build the neural network structure combining the convolution neural network and two-way gated loop unit,and use the in-depth learning method to train the microblog text emotion classification model.Secondly,build a major epidemic microblog public opinion portrait model including data support layer,tag extraction layer,feature fusion layer and portrait display layer.The data support layer of the model provides data sources for the construction of public opinion portraits;The label extraction layer uses the CNN-Bi GRU model built in Chapter3 to extract the emotional attribute labels of public opinion information,uses the BTM theme model to mine the topic attribute labels of public opinion information,combines Baidu index and time series data to extract the public opinion life cycle labels,and uses the relevant explicit indicator data to calculate the public opinion subject influence labels;The feature fusion layer combines the characteristics of various microblog public opinion features,reasonably integrates the relevant public opinion features,and provides the basis for comprehensively and profoundly portraying the public opinion portrait of major epidemic microblogs;The portrait display layer uses relevant visualization methods to display the portrait results.Finally,taking the "Shanghai 6.1 unsealing" event as the background,collect the public opinion data of microblog in the whole life cycle of the public opinion event,and conduct empirical analysis on the public opinion portrait model of microblog of major epidemic in the order of tag extraction,feature fusion,and portrait display.Based on this,the application value of the microblog public opinion portrait model in the actual situation is analyzed,and suggestions are put forward for the governance of microblog public opinion in the context of major epidemic.