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Data Analysis and Knowledge Discovery  2021, Vol. 5 Issue (6): 25-35    DOI: 10.11925/infotech.2096-3467.2020.0077
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Clustering User Groups of Public Opinion Events from Multi-dimensional Social Network
Wang Xiwei1,2,3,Jia Ruonan1(),Wei Yanan1,Zhang Liu1
1School of Management, Jilin University, Changchun 130022, China
2Research Center for Big Data Management, Jilin University, Changchun 130022, China
3Cyberspace Governance Research Center, Jilin University, Changchun 130022, China
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[Objective] User groups are the main units to disseminate public opinion. This study identifies the characteristics of user groups through clustering techniques, which could help social network companies provide better services. [Methods] With the help of Group Theory, we clustered users based on their influence, sentiments, and behaviors. First, we collected user data from the Sina Weibo. Then, we utilized Canopy and K-Means algorithms to cluster users. Finally, we visualized our findings with Neo4j and Weka. [Results] User groups of the same public opinion event were different in emotion, influence, and behaviors, while user groups from different public opinion events shared common characteristics. [Limitations] Both public opinion events in this study happened at Chinese universities, and we only collected data from Sina Weibo. [Conclusions] Based on the clustering results, we could propose effective administration strategies for each user group in the same or different public opinion events.

Key wordsMulti-dimensional      Social Network      Public Opinion      User Group      User Clustering     
Received: 03 February 2020      Published: 06 July 2021
ZTFLH:  TP393  
Fund:Special Research Project of National Development and Security (Biosafety) of Jilin University(2020JDGFAZ003);Jilin University Postgraduate Innovation Fund(101832020CX057)
Corresponding Authors: Jia Ruonan     E-mail:

Cite this article:

Wang Xiwei,Jia Ruonan,Wei Yanan,Zhang Liu. Clustering User Groups of Public Opinion Events from Multi-dimensional Social Network. Data Analysis and Knowledge Discovery, 2021, 5(6): 25-35.

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Social Network Public Opinion User Clustering Analysis Method
用户昵称 文本内容 情感分类 置信度
回头万里人常叹 真是讽刺,再次失望。 消极 0.87
diamondli99 ……可悲,中国的博士,绝对弱势群体…… 消极 0.84
小兜里满满的幸福 @中国大学生在线 @中国教育在线考研频道 @央视新闻 中性 0.94
秋桐小宅女 看调查结果。 中性 0.98
真无羽 逝者安息,实验都具有风险性,感谢他们为科学做出的贡献,安息。 积极 0.82
三七二十一个酥 ……珍惜身边的每个人吧!……愿逝者安息,实验室安全警钟长鸣…… 积极 0.88
放风筝的灰原哀 ……学术诚可贵,生命价更高,一定要注意实验安全…… 积极 0.70
叫我杏仁 岂止是难过,含辛茹苦培养出的人才,真的活不了了。 消极 0.70
CMLY丶F 切记水火无情。 中性 0.73
User Sentiment Classification Results and Confidence (Partial)
用户昵称 PageRank值 用户昵称 PageRank值
北京消防 1 736.80 澎湃新闻 897.18
江宁公安在线 52.31 南京大学 719.89
中国消防 2.24 陈迪Winston 343.35
北京交通大学 997.05 头条新闻 317.21
懒懒的周小姐 0.15 小姐姐爱学习 0.15
慎独明智 0.15 一只阿迟儿 0.15
KaiHugo 0.15 北欧DJ 0.15
PageRank Value of Users (Partial)
SSE Trend Chart of Each Event
类簇 聚类结果
“北交大”事件 “南大”事件
0 9 491 (24%) 1 675 (6%)
1 2 158 (5%) 1 485 (5%)
2 14 974 (38%) 2 729 (9%)
3 1 685 (4%) 15 144 (50%)
4 3 250 (8%) 3 360 (11%)
5 5 434 (14%) 1 446 (5%)
6 458 (1%) 2 540 (8%)
7 1 457 (4%) 1 210 (4%)
8 901 (2%) 610 (2%)
Clustering Results of Event User Group
Clustering Result of Each Event
Sentiment Distribution of Each Cluster
PageRank Value Distribution of Each Cluster
User Group Relationship Map
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