Semantic fusion and propagation model for internet public opinion data in big data environment

Abstract

In order to improve the monitoring and early warning efficiency of network public opinion, and to reveal the spread of network public opinion, an evolution process model of public opinion in the Internet is proposed, It tries to analyze the evolution mechanism of network public opinion in the Internet, and provides certain theoretical support and method guidance for network public opinion monitoring and prediction. At the semantic level, the implementation method of knowledge fusion of different levels of public opinion information resources is proposed, which is supported by semantic technologies such as the semantic web in the big data environment. Then the multi-agent modeling and simulation method is used to establish the network public opinion information communication simulation model. The attributes of each participating entity were constructed in the model, and the influence of various factors on the network crisis information transmission was analyzed. The experimental results show that the proposed verification simulation model has high credibility. By analyzing the number of "opinion leaders" in the model, the participation of netizens, the credibility of the government, the speed of information disclosure and the transparency of the public, it can improve the monitoring and early warning efficiency of network public opinion, and better reveal the propagation law of network public opinion.