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

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dc.authorid0000-0002-9619-8247
dc.contributor.authorNajafi, Alien_US
dc.contributor.authorGholipour-Shilabin, Arazen_US
dc.contributor.authorDehkharghani, Rahimen_US
dc.contributor.authorMohammadpur-Fard, Alien_US
dc.contributor.authorAsgari-Chenaghlu, Meysamen_US
dc.date.accessioned2022-09-01T07:59:23Z
dc.date.available2022-09-01T07:59:23Z
dc.date.issued2023-12
dc.departmentIşık Üniversitesi, Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.departmentIşık University, Faculty of Engineering, Department of Computer Engineeringen_US
dc.description.abstractTopic 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.en_US
dc.identifier.citationNajafi, 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/s40745-022-00426-4en_US
dc.identifier.doi10.1007/s40745-022-00426-4
dc.identifier.endpage1605
dc.identifier.issn2198-5804
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85133639965
dc.identifier.scopusqualityQ2
dc.identifier.startpage1583
dc.identifier.urihttps://hdl.handle.net/11729/4809
dc.identifier.urihttp://dx.doi.org/10.1007/s40745-022-00426-4
dc.identifier.volume10
dc.indekslendigikaynakScopusen_US
dc.institutionauthorDehkharghani, Rahimen_US
dc.institutionauthorid0000-0002-9619-8247
dc.language.isoenen_US
dc.peerreviewedYesen_US
dc.publicationstatusPublisheden_US
dc.publisherSpringer Science and Business Media Deutschland GmbHen_US
dc.relation.ispartofAnnals of Data Scienceen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectLaBSEen_US
dc.subjectSemantic similarityen_US
dc.subjectStream clusteringen_US
dc.subjectTopic detectionen_US
dc.subjectMulti agent systemsen_US
dc.subjectSemanticsen_US
dc.subjectSocial networking (online)en_US
dc.subjectHot topicsen_US
dc.subjectMulti-agent approachen_US
dc.subjectSocial mediaen_US
dc.subjectStreaming dataen_US
dc.subjectText clusteringen_US
dc.subjectCOVID-19en_US
dc.subjectEvent detectionen_US
dc.subjectTwitteren_US
dc.subjectSocial networkingen_US
dc.titleComStreamClust: a communicative multi-agent approach to text clustering in streaming dataen_US
dc.typeArticleen_US
dspace.entity.typePublication

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