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Data Analysis and Knowledge Discovery  2022, Vol. 6 Issue (1): 69-79    DOI: 10.11925/infotech.2096-3467.2021.0407
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Studying Opinion Leaders with Network Analysis and Text Mining
Sun Yu,Qiu Jiangnan()
School of Economics and Management, Dalian University of Technology, Dalian 116000, China
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Abstract  

[Objective] This paper tries to study the classification scheme for opinion leaders and evaluate their characteristics from multiple perspectives. [Methods] We proposed a method to classify opinion leaders by community division. Then, we comprehensively analyzed their influences from the dimensions of network diffusion ability and emotional dominance. We conducted an empirical analysis with Twitter data, and compared the influence of different types of opinion leaders through network analysis and text mining. [Results] Opinion leaders are identified as three communities, which rank differently in network diffusion ability and emotional dominance. The two dimensions show no correlation with an absolute value of correlation coefficient less than 0.3. Compared with the traditional weighted summing method, the two-dimensional matrix analysis can reflect the influence characteristics more comprehensively. [Limitations] In the evaluation of emotional influence, we only analyzed the original texts, and future studies will include the comments. [Conclusions] The proposed methods could analyze the degrees and characteristics of the opinion leaders' influence. It helps us understand all kinds of opinion leaders and guide the public opinion directions more effectively in risk management.

Key wordsCommunity Division      Opinion Leader      Social Network      Two-Dimensional Analytical Framework      Sentiment Analysis     
Received: 26 April 2021      Published: 22 February 2022
ZTFLH:  TP393  
Fund:Social Science Program Foundation of Liaoning Province(L19BGL001)
Corresponding Authors: Qiu Jiangnan,ORCID:0000-0001-5320-8479     E-mail: qiujiangnan@gmail.com

Cite this article:

Sun Yu, Qiu Jiangnan. Studying Opinion Leaders with Network Analysis and Text Mining. Data Analysis and Knowledge Discovery, 2022, 6(1): 69-79.

URL:

https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/10.11925/infotech.2096-3467.2021.0407     OR     https://manu44.magtech.com.cn/Jwk_infotech_wk3/EN/Y2022/V6/I1/69

Louvain Algorithm
Community Division Progress
Corresponding Results of Subjective Division and Community Division
指标 社团1 社团2 社团3
入度中心性 0.159 0.248 0.214
入接近中心性 0.337 0.389 0.373
中介中心性 0.028 0.026 0.020
PageRank 0.034 0.014 0.014
综合得分 0.113 0.138 0.120
Node Importance of Communities
Cumulative Distribution Function of Importance Index
Network of Community Division
主成分 方差贡献率/% 累计贡献率/%
1 39.296 39.296
2 33.139 72.434
3 18.395 90.830
4 9.170 100.000
Total Variance interpretation
主成分 影响力参数 真实性参数 代词参数 从属参数
1 0.704 -0.195 0.283 0.622
2 -0.128 0.741 0.654 0.079
3 -0.237 0.405 -0.585 0.662
Principal Component Coefficient Matrix
Sentiment Analysis of Communities
Two-Dimensional Framework Analysis
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