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ComStreamClust: a Communicative Multi-Agent Approach to Text Clustering in Streaming Data

Author

Listed:
  • Ali Najafi

    (Sabanci University)

  • Araz Gholipour-Shilabin

    (University of Tabriz)

  • Rahim Dehkharghani

    (Isik University)

  • Ali Mohammadpur-Fard

    (Sharif University of Technology)

  • Meysam Asgari-Chenaghlu

    (University of Tabriz)

Abstract

Topic detection is the task of determining and tracking hot topics in social media. Twitter is arguably the most popular platform for people to share their ideas with others about different issues. One such prevalent issue is the COVID-19 pandemic. Detecting and tracking topics on these kinds of issues would help governments and healthcare companies deal with this phenomenon. In this paper, we propose a novel, multi-agent, communicative clustering approach, so-called ComStreamClust for clustering sub-topics inside a broader topic, e.g., the COVID-19 and the FA CUP. The proposed approach is parallelizable, and can simultaneously handle several data-point. The LaBSE sentence embedding is used to measure the semantic similarity between two tweets. ComStreamClust has been evaluated by several metrics such as keyword precision, keyword recall, and topic recall. Based on topic recall on different number of keywords, ComStreamClust obtains superior results when compared to the existing methods.

Suggested Citation

  • Ali Najafi & Araz Gholipour-Shilabin & Rahim Dehkharghani & Ali Mohammadpur-Fard & Meysam Asgari-Chenaghlu, 2023. "ComStreamClust: a Communicative Multi-Agent Approach to Text Clustering in Streaming Data," Annals of Data Science, Springer, vol. 10(6), pages 1583-1605, December.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:6:d:10.1007_s40745-022-00426-4
    DOI: 10.1007/s40745-022-00426-4
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    References listed on IDEAS

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    1. Braznev Sarkar & Malay Bhattacharyya, 2021. "Spectral Algorithms for Streaming Graph Analysis: A Survey," Annals of Data Science, Springer, vol. 8(4), pages 667-681, December.
    2. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
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