基于EKSC算法的网络事件热度预测方法

摘要

随着互联网的发展,有效地对网络舆情进行监管和引导对社会的和谐稳定具有重要意义,网络事件的热度预测是舆情监管的重要组成部分.针对传统方法在预测的过程中忽视了事件时间序列中蕴含的时态信息和关联性,提出了一种基于EKSC算法的网络热点事件热度预测模型.该模型使用EKSC算法对每类已知网络舆情事件的时间序列进行聚类,并构建类模型库.对待预测事件已知的热度时间序列进行缩放变化,并使用最小二乘法选取类模型库中均方误差和最小的模型对该事件进行预测.实验表明,该方法能够对网络热点事件的热度进行有效的预测.%With the rapid development of the Internet,how to effectively monitor and guide the public opinion on the Internet is of great significance to the social stability.The prediction of network events' hotness is an important part of public opinion supervision.In view of the fact that the traditional method ignores the temporal information and the relevance contained in the event time series in the process of prediction,a prediction model based on EKSC algorithm is proposed.The model uses the EKSC algorithm to cluster the time series of known network public opinion events of each class and construct a class model library.The time sequence of the know hotness in the predicted event is scaled.The least square method is used to predict the event by selecting the model with the minimum mean square error in the class library.Experimental results show that this method can effectively predict the hotness of network events.