ComStreamClust: a communicative multi-agent approach to text clustering in streaming data
Yükleniyor...
Tarih
2023-12
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Springer Science and Business Media Deutschland GmbH
Erişim Hakkı
info:eu-repo/semantics/closedAccess
Özet
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.
Açıklama
Anahtar Kelimeler
LaBSE, Semantic similarity, Stream clustering, Topic detection, Multi agent systems, Semantics, Social networking (online), Hot topics, Multi-agent approach, Social media, Streaming data, Text clustering, COVID-19, Event detection, Twitter, Social networking
Kaynak
Annals of Data Science
WoS Q Değeri
Scopus Q Değeri
Q2
Cilt
10
Sayı
6
Künye
Najafi, A., Gholipour-Shilabin, A., Dehkharghani, R., Mohammadpur-Fard, A. & Asgari-Chenaghlu, M. (2023). ComStreamClust: a communicative multi-agent approach to text clustering in streaming data. Annals of Data Science, 10(6) 1583-1605. doi:10.1007/s40745-022-00426-4
