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Sentiment community detection: exploring sentiments and relationships in social networks

Author

Listed:
  • Dong Wang

    (Ocean University of China)

  • Jiexun Li

    (Western Washington University)

  • Kaiquan Xu

    (Nanjing University)

  • Yizhen Wu

    (Nanjing University)

Abstract

Social networking sites (SNS), which allow users to express opinions on products/services, have become an important channel and platform for enterprises to acquire and trace users’ sentiments in order to design appropriate business strategies and online marketing campaigns. However, with the large number of users and complex user relationships on SNS, effectively capturing these sentiments for business decision support is still a big challenge. In this study we introduce the concept of “Sentiment Community,” a group of users who are closely connected and highly consistent in their sentiments about one product/service. Discovering such sentiment communities would be very valuable to enterprises for customer segmentation and target marketing. Taking into account both connections and sentiments, we propose two methods to discover sentiment communities by adopting the optimization models of semi-definite programming (SDP). Our experimental evaluations demonstrated great performances for the proposed methods. This study opens the doors to effectively explore users’ sentiments on SNS for business decision making.

Suggested Citation

  • Dong Wang & Jiexun Li & Kaiquan Xu & Yizhen Wu, 2017. "Sentiment community detection: exploring sentiments and relationships in social networks," Electronic Commerce Research, Springer, vol. 17(1), pages 103-132, March.
    • Handle: RePEc:spr:elcore:v:17:y:2017:i:1:d:10.1007_s10660-016-9233-8
    DOI: 10.1007/s10660-016-9233-8
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    References listed on IDEAS

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    1. Chrysanthos Dellarocas, 2006. "Strategic Manipulation of Internet Opinion Forums: Implications for Consumers and Firms," Management Science, INFORMS, vol. 52(10), pages 1577-1593, October.
    2. G. Agarwal & D. Kempe, 2008. "Modularity-maximizing graph communities via mathematical programming," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 66(3), pages 409-418, December.
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    Cited by:

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    2. Yanjie Xu & Tao Ren & Shixiang Sun, 2022. "Community Detection Based on Node Influence and Similarity of Nodes," Mathematics, MDPI, vol. 10(6), pages 1-15, March.
    3. Jitendra Kumar Rout & Kim-Kwang Raymond Choo & Amiya Kumar Dash & Sambit Bakshi & Sanjay Kumar Jena & Karen L. Williams, 2018. "A model for sentiment and emotion analysis of unstructured social media text," Electronic Commerce Research, Springer, vol. 18(1), pages 181-199, March.
    4. Joseph, Simmi Marina & Citraro, Salvatore & Morini, Virginia & Rossetti, Giulio & Stella, Massimo, 2023. "Cognitive network neighborhoods quantify feelings expressed in suicide notes and Reddit mental health communities," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 610(C).
    5. Kejia Chen & Jian Jin & Zheng Zhao & Ping Ji, 2022. "Understanding customer regional differences from online opinions: a hierarchical Bayesian approach," Electronic Commerce Research, Springer, vol. 22(2), pages 377-403, June.
    6. Swarup Chattopadhyay & Tanmay Basu & Asit K. Das & Kuntal Ghosh & Late C. A. Murthy, 2021. "Towards effective discovery of natural communities in complex networks and implications in e-commerce," Electronic Commerce Research, Springer, vol. 21(4), pages 917-954, December.

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