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| Identifying Key Users and Topics from Online Learning Community |
Cai Yongming1( ),Liu Lu1,Wang Kewei2 |
1Business School, University of Jinan, Jinan 250002, China
2School of Economics and Management, Inner Mongolia University of Technology, Huhhot 010051, China |
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Abstract [Objective] This study automatically analyzes resources of a virtual learning community, aiming to address the issue of information overload. [Methods] We proposed a hyper-network LDA model based on the user-document-word cube. Then, we modified this LDA model with the help of word and user analysis. Finally, we improved the cohesiveness of topics in the hyper-network LDA model, through increasing the distribution probability of closely connected words or users for the same topics. [Results] Compared to the traditional social network analysis methods, the proposed LDA model can identify important users, key topics and the relationship among them, as well as user preferences with frequency matrix of user-vocabulary and distribution probability of user-topic. [Limitations] Hyper-network analysis theory is still developing and we only studied the weighted un-directed network, which does not include the relationship of posting and replying. [Conclusions] The hyper-network LDA model effectively analyzes topics of short texts and online interactions, which are of significance to users and online learning community managers.
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Received: 08 October 2019
Published: 07 July 2020
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Corresponding Authors:
Cai Yongming
E-mail: cymujn@163.com
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