Research on the Early-Warning Model of Network Public Opinion of Major Emergencies
| Autor: | Xi-Gao Shao, Li-Jie Peng, Wan-Ming Huang |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
General Computer Science Computer science 020209 energy GA-BP neural network Decision tree Computers and Information Processing 02 engineering and technology Public opinion Naive Bayes classifier 0202 electrical engineering electronic engineering information engineering medicine General Materials Science Social media early warning index system Warning system business.industry Major emergencies internet public opinion Public health General Engineering COVID-19 CRITIC Data science TK1-9971 020201 artificial intelligence & image processing The Internet Electrical engineering. Electronics. Nuclear engineering Construct (philosophy) business Mathematics |
| Zdroj: | Ieee Access IEEE Access, Vol 9, Pp 44162-44172 (2021) |
| ISSN: | 2169-3536 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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