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Topic Network Analysis Based on Co-Occurrence Time Series Clustering

Author

Listed:
  • Weibin Lin

    (College of Business Administration, Huaqiao University, Quanzhou 362021, China
    TSL Business School, Quanzhou Normal University, Quanzhou 362021, China)

  • Xianli Wu

    (College of Business Administration, Huaqiao University, Quanzhou 362021, China)

  • Zhengwei Wang

    (College of Business Administration, Huaqiao University, Quanzhou 362021, China)

  • Xiaoji Wan

    (College of Business Administration, Huaqiao University, Quanzhou 362021, China)

  • Hailin Li

    (College of Business Administration, Huaqiao University, Quanzhou 362021, China
    Research Center of Applied Statistics and Big Data, Huaqiao University, Xiamen 361021, China)

Abstract

Traditional topic research divides similar topics into the same cluster according to clustering or classification from the perspective of users, which ignores the deep relationship within and between topics. In this paper, topic analysis is achieved from the perspective of the topic network. Based on the initial core topics obtained by the keyword importance and affinity propagation clustering, co-occurrence time series between topics are constructed according to time sequence and topic frequency. Subsequence segments of each topic co-occurrence time series are divided by sliding windows, and the similarity between subsequence segments is calculated. Based on the topic similarity matrix, the topic network is constructed. The topic network is divided according to the community detection algorithm, which realizes the topic re-clustering and reveals the deep relationship between topics in fine-grained. The results show there is no relationship between topic center representation and keyword popularity, and topics with a wide range of concepts are more likely to become topic network centers. The proposed approach takes into account the influence of time factors on topic analysis, which not only expands the analysis in the field of topic research but also improves the quality of topic research.

Suggested Citation

  • Weibin Lin & Xianli Wu & Zhengwei Wang & Xiaoji Wan & Hailin Li, 2022. "Topic Network Analysis Based on Co-Occurrence Time Series Clustering," Mathematics, MDPI, vol. 10(16), pages 1-17, August.
  • Handle: RePEc:gam:jmathe:v:10:y:2022:i:16:p:2846-:d:884949
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    References listed on IDEAS

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    Cited by:

    1. Xiaoji Wan & Fen Chen & Hailin Li & Weibin Lin, 2022. "Potentially Related Commodity Discovery Based on Link Prediction," Mathematics, MDPI, vol. 10(19), pages 1-27, October.

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