Twitter topic fuzzy fingerprints

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.date.updated2022-06-21T12:16:51Z
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.description.versioninfo:eu-repo/semantics/acceptedVersion
dc.event.date2014
dc.event.locationBeijingeng
dc.event.typeConferênciapt
dc.identifier.doi10.1109/FUZZ-IEEE.2014.6891781
dc.identifier.issn1098-7584
dc.identifier.urihttp://hdl.handle.net/10071/25677
dc.journal2014 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE): Proceedings
dc.language.isoeng
dc.peerreviewedyes
dc.publisherIEEE
dc.relationPEstOE/EEI/LA0021/2013
dc.relationinfo:eu-repo/grantAgreement/FCT/3599-PPCDT/PTDC%2FIVC-ESCT%2F4919%2F2012/PT
dc.rightsopen access
dc.subject.fosDomínio/Área Científica::Ciências Naturais::Ciências Físicaspor
dc.titleTwitter topic fuzzy fingerprintseng
dc.typeconferenceObject
degois.publication.locationBeijingeng
degois.publication.titleTwitter topic fuzzy fingerprintseng
dspace.entity.typePublicationen
iscte.alternateIdentifiers.scopus2-s2.0-84912535475
iscte.alternateIdentifiers.wosWOS:WOS:000350793500112
iscte.identifier.cienciahttps://ciencia.iscte-iul.pt/id/ci-pub-18038

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