基于舆情大数据的网络群体性事件动态识别模型与应对策略研究

Dynamic Identification Model and Coping Strategy of Network Mass Incidents Based on Big Data of Public Opinion

【目的/意义】随着互联网在社会中的影响力逐渐增大,面对网络群体性事件对社会生活的冲击,需把握网络群体性事件的演化规律,确定事件类别,提炼事件特征,基于不同类别的网络群体性事件,提出有针对性的应对措施。【方法/过程】通过LDA主题模型与K-means算法相结合,利用LDA模型实现文本潜在语义的识别,最终运用SVM算法进行网络群体性事件聚类分析,得到五类网络群体性事件。【结果/结论】构建的网络群体性事件动态识别模型,通过大量的文本训练,在事件聚类数为5时具有良好的解释性,完成了网络群体性事件的客观分类,分别为:经济型、社会型、文化型、民族型和环境型,为政府分类应对策略提供依据。【创新/局限】利用LDA主题模型和Kmeans算法,减少了模型的迭代次数,确定最佳主题数,提高了网络群体性事件识别结果的准确性,但是运用慧科新闻数据库搜集到的文本数据范围有限,且分类结果反应的事件特征具有一定局限性,后续研究可进一步扩大动态文本数据库,对分类算法进行改进和深化。

【Purpose/significance】With the increasing influence of the Internet in society,facing the impact of network mass incidents on social life,it is necessary to grasp the evolution law of network mass incidents,determine the type of event,refine the characteristics of incidents,and put forward targeted countermeasures based on different types of network mass incidents.【Method/process】Through the combination of LDA topic model and K-means algorithm,LDA model is used to realize the recognition of text potential semantics.Finally,support vector machine algorithm is used to perform cluster analysis of network group events,and obtained five types of network group events.【Result/conclusion】To build network dynamic identification model of mass incidents,through a large number of text training,in the event the clustering number for 5 good explanatory,completed the network of mass incidents objective classification,are:economy,society,culture form,ethnic group and the environment,provide the basis for the government classification strategy.【Innovation/limitation】This paper used the LDA model and K-means algorithm,reduce the number of iterations of the model,to determine the best theme,improve the accuracy of the network group incidents recognition results,but the use of text data collected by the wisers news database scope finitely,and the classification results reflect the features of events has certain limitations,further research can further expand the dynamic text database,improve and deepen the classification algorithm.