聚类算法在校园网络舆情监控与预警技术中的应用研究  

Research on the Application of Clustering Algorithm in Campus Network Public Opinion Monitoring and Early Warning Technology

作　　者：王越 WANG Yue(Shandong Labor Vocational&Technical College,Jinan Shandong 250022,China)

机构地区：[1]山东劳动职业技术学院,山东济南250022

出　　处：《信息与电脑》2021年第16期59-61,共3页Information & Computer

基　　金：山东省职业教育教学改革研究项目“基于大数据学生画像的高职多层次人才培养模式改革研究”(项目编号:H05G141005)。

摘　　要：近些年,随着网络自媒体的迅猛发展,学生在校园内获取信息的渠道更加广泛,发表见解的方式变得更加便捷。这体现着学生言论自由程度的提升,但随之而来的信息渗透和舆论传导,使新媒体舆情呈现出明显的大数据特征。因此,如何将数据挖掘与网络舆情分析相结合,对于稳定校园舆情工作具有十分重大的意义。本文重点研究了聚类算法在校园网络舆情分析中的应用,通过对文本数据进行相似度的计算,结合K-means算法对其进行热门话题提取检测,从而实现网络舆情数据的监控和预警。In recent years, with the rapid development of network we media, students have more extensive channels to obtain information on campus, and the way to express their opinions has become more convenient. This reflects the improvement of students’ freedom of speech, but the subsequent information penetration and public opinion guidance make the public opinion of new media show obvious characteristics of big data. So, how to combine data mining with network public opinion analysis is of great significance to stabilize campus public opinion. This paper focuses on the application of clustering algorithm in campus network public opinion monitoring. Through the similarity calculation of text data, combined with K-means algorithm, it extracts and detects hot topics, so as to realize the monitoring and early warning of network public opinion data.

关 键 词：相似度计算 K-MEANS聚类 舆情监控 数据分析 

分 类 号：TP311.13[自动化与计算机技术—计算机软件与理论] G647[自动化与计算机技术—计算机科学与技术]