Research on Prediction of News Public Opinion Guiding Power Based on Neural Network

Abstract

The development of big data technology and the popularity of we-media platforms make the prediction of news public opinion more complicated, which means that the complexity and dynamic nature of news public opinion require higher accuracy of prediction. With the rapid popularization of we media technology, traditional single-model algorithm is difficult to effectively predict network public opinion under the current background. Therefore, this paper proposes an algorithm based IGA-RBF neural network to deal with the complicated news public opinion prediction. Firstly, the ARMA (autoregressive moving average model) prediction model is constructed and the BRF neural network is combined. Then IGA is introduced to optimize BRF neural network, and the column vector of output matrix of hidden layer is optimized globally. The algorithm uses k-means clustering to select parameters in RBF network. The experimental results demonstrate that the model algorithm makes up for the shortcomings of the single prediction algorithm, improves the accuracy of prediction, and has better prediction results of public opinion trends.