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

Ali Najafi (), Araz Gholipour-Shilabin (), Rahim Dehkharghani (), Ali Mohammadpur-Fard () and Meysam Asgari-Chenaghlu ()
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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

Annals of Data Science, 2023, vol. 10, issue 6, No 9, 1583-1605

Abstract: 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.

Keywords: Data stream; LaBSE; Semantic similarity; Stream clustering; Topic detection (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s40745-022-00426-4

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