基于超网络的企业微博用户聚类研究及特征分析  

Analyzing&Clustering Enterprise Microblog Users with Supernetwork

作　　者：席运江[1] 杜蝶蝶 廖晓 仉学红 Xi Yunjiang;Du Diedie;Liao Xiao;Zhang Xuehong(School of Business Administration,South China University of Technology,Guangzhou 510641,China;School of Internet Finance and Information Engineering,Guangdong University of Finance,Guangzhou 510521,China)

机构地区：[1]华南理工大学工商管理学院,广州510641 [2]广东金融学院互联网金融与信息工程学院,广州510521

出　　处：《数据分析与知识发现》2020年第8期107-118,共12页Data Analysis and Knowledge Discovery

基　　金：国家自然科学基金项目“基于超网络的企业微博知识挖掘及整合方法研究”(项目编号:71371077)的研究成果之一。

摘　　要：【目的】提出一种对多维用户兴趣数据的集成建模方法,并在此基础上研究用户兴趣的谱聚类方法。【方法】以"三只松鼠"微博数据为实例,采用超网络模型对微博内容及用户互动数据进行整合建模,构建互动兴趣度指数,并结合谱聚类算法划分用户群。通过Silhouette Coefficient及Davies-Bouldin方法对实验结果进行评估。【结果】对比三类用户特征向量的最优聚类效果,发现当k取15时,基于话题互动超网络特征向量的聚类DB值达到0.57,效果优于基于互动数据或博文内容的特征向量,类群之间分布更均匀,类群内部也更紧致。【局限】用户特征数据的选取未能全面涵盖。此外,不同维度数据对用户兴趣的影响程度或可进一步探索。【结论】通过对企业微博用户群体分布情况和兴趣特征的分析,提出对应的维护和营销建议,有助于指导企业更好地发现用户兴趣,提升微博营销效果。[Objective]This paper proposes an integrated modeling method to process multi-dimensional user interest data,aiming to examine the spectral clustering method for analyzing user interests.[Methods]First,we retrieved Weibo(Microblog)data of"Three Squirrels"and used supernetwork model to integrate the modeling of contents and user interaction data.Then,we constructed an interactive interest index and grouped the users with spectral clustering algorithm.Finally,we evaluated the clustering results with the Silhouette Coefficient and Davies-Bouldin methods.[Results]We found that the clustering DB value reached 0.57(k was set at 15),which was evenly distributed.[Limitations]More research is needed to further explore user characteristic data and the impacts of different data dimensions on user interests.[Conclusions]This study proposes maintenance and marketing suggestions for enterprise Weibo profiles,which will help them identify user interests and improve marketing effectiveness.

关 键 词：超网络 企业微博 用户兴趣 谱聚类 

分 类 号：G206[文化科学—传播学] F274[经济管理—企业管理]