基于事理知识图谱的舆情推演方法

一直以来舆情态势发展的多元性、复杂性使其难以有效管控,一些负面舆情会激化矛盾,给社会安定带来不利影响.提出了一种基于事理知识图谱的舆情事件推演方法,通过神经网络挖掘事件因果逻辑,连接因果事件构成事理知识图谱.向量化事件节点以融合归并相似节点降低图谱冗余,增强图谱泛化性.根据事理知识图谱反映的发展逻辑对目标舆情事件的演化趋势进行预测.以自然灾害舆情事件为例,实验结果表明提出的方法能够有效预测舆情事件发展方向,可以为舆情监管提供一定支持.

Public Opinion Deduction Based on Event Logic Graph

The diverse and complex trend of public opinion has long made it difficult to manage. Negative public opinion will intensify contradictions, bringing adverse effects to social stability. Then a method of public opinion deduction based on the event knowledge graph is proposed. The causal logic of the event is mined through the neural network, and the event knowledge graph is drawn after causal events are connected. Vectorized event nodes can merge into similar nodes to reduce map redundancy while enhancing map generalization. Besides, the evolution of target public opinion events can be predicted based on the deductive logic indicated in the event knowledge graph. With a public opinion event related to a natural disaster as an example, the experimental results prove that the proposed method can reliably predict the trend of the event, supporting public opinion supervision.