School of Cyber Science and Engineering, Sichuan University
要想实现对纷繁复杂的网络舆情的监控和管理，预防舆情危机的突发状况，一个关键的解决方案就是对网络舆情事件的发展趋势进行预测. 然而，目前针对舆情演变预测的研究工作却十分有限，尤其是社交网络环境中的舆情演变预测. 本文将评论文本的情感值作为演变预测的对象，利用情感词和舆情事件中评论文本的语义相似度，为事件发展的每个时间段都构造一个对应的图结构，再结合门控循环单元（GRU）与图注意力网络（GAT）对情感时间序列进行预测. 为了验证模型的有效性，本文以Twitter中弗洛伊德事件的评论文本作为数据集，开展与基于图卷积网络的预测模型的对比实验. 实验结果表明，本文提出模型的R2决定系数为0.569，平均绝对误差（MAE）、均方误差（MSE）和均方根误差（RMSE）均小于基于图卷积网络的预测模型，能较好地实现舆情事件中评论文本的情感演变预测.
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.
引用本文格式： 彭思琪,周安民,廖珊,周雨婷,刘德辉,文雅. 基于图注意力网络的舆情演变预测研究[J]. 四川大学学报: 自然科学版, 2022, 59: 013004.复制