Forecast method of public opinion evolution based on graph attention network

Abstract:

The evolution prediction of network public opinion events is a key step in monitoring and management of the complicated network public opinion, as well as in preventing the sudden outbreak of public opinion crisis； however, less attention is paid on the public opinion evolution prediction, especially in the social network. In this paper, an evolution prediction model for public sentiment events on the social network is proposed, in which the sentiment value of comment texts is termed as the object of evolution prediction and the semantic similarity between sentiment words and comment texts concerning some public events is used to construct a corresponding graph structure for each period of event development, then a model for predicting the sentiment time series is constructed by combining gated recurrent unit (GRU) and graph attention network (GAT). To further verify the effectiveness of the proposed model, the text of comments on the Freud event in Twitter is selected as the dataset and the comparative experiments are conducted with the prediction model based on graph convolutional network. The experimental results show that the R2 coefficient of determination of the proposed model is 0.569, the mean absolute error (MAE), mean square error (MSE) and root mean square error (RMSE) are all smaller than those of the graph convolutional network-based prediction model, which can demonstrate the better performance of the proposed model concerning the evolution prediction of the public sentiment events under the social network environment.