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Community Detection on Social Networks With Sentimental Interaction

Author

Listed:
  • Bingdao Feng

    (Tianjin University, China)

  • Fangyu Cheng

    (Harbin Institute of Technology, China)

  • Yanfei Liu

    (Tianjin University, China)

  • Xinglong Chang

    (Tianjin University, China)

  • Xiaobao Wang

    (Tianjin University, China)

  • Di Jin

    (Tianjin University, China)

Abstract

Many studies on community detection are mainly based on the similarity in friendship between users. Recent studies have started to explore node contents to identify semantically meaningful communities. However, the sentimental interaction information which plays an important role in community detection is often ignored. By analyzing and utilizing the abundant sentimental interaction information, one can not only more precisely identify the communities, but also discover the interesting interactions and conflicts between these communities. Based on this concept, the authors propose a new Community Sentiment Diffusion Detection Model (CSDD), which utilizes sentimental information embedded in forward posts. Furthermore, the authors present an efficient variational algorithm for model inference. The community detection results have been verified on two large Twitter datasets. It is experimentally demonstrated that we can provide a fine-grained view of sentimental interaction between communities and discover the mechanism of sentiment diffusion between communities.

Suggested Citation

  • Bingdao Feng & Fangyu Cheng & Yanfei Liu & Xinglong Chang & Xiaobao Wang & Di Jin, 2024. "Community Detection on Social Networks With Sentimental Interaction," International Journal on Semantic Web and Information Systems (IJSWIS), IGI Global, vol. 20(1), pages 1-23, January.
    • Handle: RePEc:igg:jswis0:v:20:y:2024:i:1:p:1-23
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    References listed on IDEAS

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    1. Mehmet Balcilar & Elie Bouri & Rangan Gupta & Clement Kweku Kyei, 2021. "High-Frequency Predictability of Housing Market Movements of the United States: The Role of Economic Sentiment," Journal of Behavioral Finance, Taylor & Francis Journals, vol. 22(4), pages 490-498, October.
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