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  1. ComStreamClust: a communicative multi-agent approach to text clustering in streaming data
ComStreamClust: a communicative multi-agent approach to text clustering in streaming data
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ComStreamClust: a communicative multi-agent approach to text clustering in streaming data

Najafi, A.

  1. DOI:10.1007/s40745-022-00426-4
  2. Main Entry: Najafi, A.
  3. Title:ComStreamClust: a communicative multi-agent approach to text clustering in streaming data.
  4. Publisher:Springer Science and Business Media Deutschland GmbH, 2022.
  5. 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. © 2022, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature
  6. Notes:Sharif Repository
  7. Subject:Multi agent systems.
  8. Subject:Semantics.
  9. Subject:Social networking (online)
  10. Subject:Data stream.
  11. Subject:Hot topics.
  12. Subject:LaBSE.
  13. Subject:Multi-agent approach.
  14. Subject:Semantic similarity.
  15. Subject:Social media.
  16. Subject:Stream clustering.
  17. Subject:Streaming data.
  18. Subject:Text Clustering.
  19. Subject:Topic detection.
  20. Subject:COVID-19.
  21. Added Entry:Gholipour Shilabin, A.
  22. Added Entry:Dehkharghani, R.
  23. Added Entry:Mohammadpur Fard, A.
  24. Added Entry:Asgari Chenaghlu, M.
  25. Added Entry:Sharif University of Technology.
  26. Source: Annals of Data Science ; 2022 ; 21985804 (ISSN)
  27. Web Site:https://link.springer.com/article/10.1007/s40745-022-00426-4

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