Please use this identifier to cite or link to this item: http://hdl.handle.net/10071/25677
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dc.contributor.authorRosa, H.-
dc.contributor.authorBatista, F.-
dc.contributor.authorCarvalho, J.-
dc.date.accessioned2022-06-21T11:17:47Z-
dc.date.available2022-06-21T11:17:47Z-
dc.date.issued2014-
dc.identifier.issn1098-7584-
dc.identifier.urihttp://hdl.handle.net/10071/25677-
dc.description.abstractIn this paper we propose to approach the subject of Twitter Topic Detection using a new technique called Topic Fuzzy Fingerprints. A comparison is made with two popular text classification techniques, Support Vector Machines (SVM) and k-Nearest Neighbours (kNN). Preliminary results show that Twitter Topic Fuzzy Fingerprints outperforms the other two techniques achieving better Precision and Recall, while still being much faster, which is an essential feature when processing large volumes of streaming data.eng
dc.language.isoeng-
dc.publisherIEEE-
dc.relationPEstOE/EEI/LA0021/2013-
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FIVC-ESCT%2F4919%2F2012/PT-
dc.rightsopenAccess-
dc.titleTwitter topic fuzzy fingerprintseng
dc.typeconferenceObject-
dc.event.typeConferênciapt
dc.event.locationBeijingeng
dc.event.date2014-
dc.peerreviewedyes-
dc.journal2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE): Proceedings-
degois.publication.locationBeijingeng
degois.publication.titleTwitter topic fuzzy fingerprintseng
dc.date.updated2022-06-21T12:16:51Z-
dc.description.versioninfo:eu-repo/semantics/acceptedVersion-
dc.identifier.doi10.1109/FUZZ-IEEE.2014.6891781-
dc.subject.fosDomínio/Área Científica::Ciências Naturais::Ciências Físicaspor
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-18038-
iscte.alternateIdentifiers.wosWOS:WOS:000350793500112-
iscte.alternateIdentifiers.scopus2-s2.0-84912535475-
Appears in Collections:IT-CRI - Comunicações a conferências internacionais

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