大数据环境下基于K均值聚类的网络伪舆情分类研究

Research on Classification of Network Pseudo-Public Opinion Based on K-Means Clustering in the Environment of Big Data

为了解决网络伪舆情事件分类过于主观以及分类标准模糊的问题.该文基于网络大数据建立一个全新的伪舆情识别指标体系,在此基础上,收集过去一年中网络伪舆情事件的相关指标数据,基于Python软件利用经典的K均值聚类算法对网络伪舆情事件进行聚类分析,得到三个类别的网络伪舆情事件集,分析总结各类伪舆情事件本身的特点.该文为网络伪舆情的识别与分类提供了一种全新的方法,为相关部门利用网络大数据准确控制各类伪舆情提供参考.

In order to solve the problem that the classification of network pseudo-public opinion events is too subjective and the classification standard is fuzzy,this paper establishes a new pseudo-public opinion identification index system based on network big data.On this basis,collects relevant index data of network pseudo-public opinion events in the past year.Based on Python software,the classic K-means clustering algorithm is used to conduct network pseudo-public opinion events.Cluster analysis is used to obtain three types of network pseudo-public opinion event sets,and analyze and summarize the characteristics of various pseudo-public opinion events.This article provides a brand-new method for the identification and classification of false public opinion on the Internet,and provides a reference for relevant departments to accurately control various types of false public opinion using network big data.