Open AccessPublic Opinion Prediction Model of Food Safety Events Network Based on BP Neural Network
Author(s) -
Zheng Chen,
Yafei Song,
Youchun Ma
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/719/1/012078
Subject(s) - microblogging , public opinion , crawling , computer science , artificial neural network , social media , event (particle physics) , social network (sociolinguistics) , identity (music) , artificial intelligence , data mining , world wide web , political science , medicine , physics , anatomy , quantum mechanics , politics , acoustics , law
The food safety incident network public opinion has the characteristics of wide audience, complex and changeable, and bad influence. It is of great theoretical and practical significance to study the behavioral and influencing factors of microblog public opinion forwarding in this kind of event. This paper summarizes and enriches the index factors affecting the network’s public opinion forwarding volume. Combined with BP neural network algorithm, this paper constructs a network public opinion forwarding behavior prediction model, and applies and verifies it by crawling Sina Weibo food safety event microblog data. The results show that the introduction of fan activity has a certain weight ratio, which has a greater impact on the forwarding of public opinion in food safety events network with identity authentication, hot search, hypertext and other indicators, and the prediction model combined with BP neural network has a better prediction effect.