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Data Analysis and Knowledge Discovery
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Research and Feature Analysis of Enterprise Microblog User Clustering Based on Supernetwork
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)
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[Objective] This paper proposes an integrated modeling method for multi-dimensional user interest data, and on this basis, studies the spectral clustering method of user interests.

[Methods] The paper takes the Weibo data of Three Squirrels as example.First,We use a supernetwork model to integrate the modeling of weibo content and user interaction data. Then, we construct an interactive interest index.Third, we combine the spectral clustering algorithm to divide the user groups.Finally, The clustering results are evaluated by the Silhouette Coefficient and Davies-Bouldin methods.

[Results] Comparing the optimal clustering effect of the three types of user feature vectors, it’s show that the clustering DB value of feature vectors based on topic interactive supernetworks is 0.57 when K is 15. Which is lower than feature vectors based on interactive data or blog content, with more even distribution among groups and tighter within groups.

[Limitations] More user characteristic data needs to be covered. In addition, the impact of different dimensions of data on user interests may be further explored.

[Conclusions] By analyzing the distribution and interest characteristics of enterprise microblog user groups, we propose corresponding maintenance and marketing suggestions, which will help companies better discover user interests and improve microblog marketing effect.

Key words supernetwork      enterprise microblog      user interests      spectral clustering      
Published: 21 May 2020
ZTFLH:  G206  

Cite this article:

Xi Yunjiang, Du Diedie, Liao Xiao, Zhang Xuehong. Research and Feature Analysis of Enterprise Microblog User Clustering Based on Supernetwork . Data Analysis and Knowledge Discovery, 0, (): 1-.

URL: 2096-3467.2020.0091     OR

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