Research on the Early-Warning Model of Network Public Opinion of Major Emergencies

Abstract

The rapid development of Internet in recent years has led to a proliferation of social media networks as people who can gather online to share information, knowledge, and opinions. However, the network public opinion tends to generate strongly misleading and a large number of messages can cause shocks to the public once major emergencies appear. Therefore, we need to make correct prediction regarding and timely identify a potential crisis in the early warning of network public opinion. In view of this, this study fully considers the features of development and the propagation characteristics, so as to construct a network public opinion early warning index system that includes 4 first-level indicators and 13 second-level indicators. The weight of each indicator is calculated by the "CRITIC" method, so that the comprehensive evaluation value of each time point can be obtained and the early warning level of internet public opinion can be divided. Then, the Back Propagation neural network based on Genetic Algorithm (GA-BP) is used to establish a network public opinion early warning model. Finally, the major public health emergency, COVID-19 pandemic, is taken as a case for empirical analysis. The results show that by comparing with the traditional classification methods, such as BP neural network, decision tree, random forest, support vector machine and naive Bayes, GA-BP neural network has a higher accuracy rate for early warning of network public opinion. Consequently, the index system and early warning model constructed in this study have good feasibility and can provide references for related research on internet public opinion. INDEX TERMS Major emergencies, internet public opinion, Early warning index system, COVID-19, CRITIC, GA-BP neural network.

Interoperability of Multimedia Network Public Opinion Knowledge Base Group Based on Multisource Text Mining

“…It has become one of the main problems that domestic and foreign scholars need to solve that the relevant government departments provide crossplatform, cross-system knowledge association, analysis, and text mining [1,2]. e interoperation of different types of public opinion knowledge base groups has become the main means to complete the crossover and integration of multimedia public opinion data information under big data [3,4], and it has also become an effective means for the acquisition, preservation, and use of big data information for multimedia network public opinion [5]. Public opinion data information, as an effective expression and reaction to public opinion, is also a way of responding to the public's thinking situation [6].…”

Sentiment Analysis: An ERNIE-BiLSTM Approach to Bullet Screen Comments

“…In this study, comments are divided into about 16,000 training comments, 2000 validation comments, and 2000 test comments in a ratio of 8:1:1. The training set, validation set, and test set include 50% positive comments and 50% negative comments [ 22 ]. As using the same dataset for comparative experiments can effectively control variables and reduce the distortion of comparative experiments, this study uses the same dataset for model retraining and testing.…”

Application of edge computing combined with deep learning model in the dynamic evolution of network public opinion in emergencies

“…This research was of great significance to service providers in formulating management strategies for consumers to choose on-demand services. Peng et al [ 11 ] studied the NPO-oriented Early Warning Model (EWM) for major emergencies, fully considered the development features and communication features, and built an NPO-oriented early warning index system including four primary indicators and 13 secondary indicators. The results showed that the index system and early warning model were feasible and could provide a reference for the related NPO research.…”