Please use this identifier to cite or link to this item: http://hdl.handle.net/10773/21096
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dc.contributor.authorVilaça, Afonsopt
dc.contributor.authorAntunes, M.pt
dc.contributor.authorGomes, D.pt
dc.contributor.authorAguiar, Rui L.pt
dc.date.accessioned2017-12-12T12:49:35Z-
dc.date.available2017-12-12T12:49:35Z-
dc.date.issued2015-09-
dc.identifier.urihttp://hdl.handle.net/10773/21096-
dc.description.abstractSharing live experiences in social networks is a growing trend. That includes posting comments and sentiments about TV programs. Automatic detection of messages with contents related to TV allows a numerous quantity of applications in the industry of entertainment information. This paper describes a system that is capable of detecting TV highlights in one of the most important social networks - Twitter. Combining Twitter’s messages and information from an Electronic Programming Guide (EPG) we built a model that matches tweets with TV programs with an accuracy over 80%. Our model required the construction of semantic profiles for the Portuguese language. These semantic profiles are used to identify the most representative tweets as highlights of a TV program. Far from finished, we intend to further develop our system to take advantage of external metadata in order to improve matching rates.pt
dc.language.isoengpt
dc.publisherInstituto de Telecomunicaçõespt
dc.rightsopenAccesspor
dc.titleTVPulse: detecting TV highlights in Social Networkspt
dc.typeconferenceObjectpt
dc.peerreviewedyespt
ua.distributioninternationalpt
degois.publication.title10th Conference on Telecommunications Conftelept
degois.publication.title10th Conference on Telecommunications Conftele-
dc.relation.publisherversionhttp://atnog.av.it.pt/content/tvpulse-detecting-tv-highlights-social-networkspt
Appears in Collections:DETI - Comunicações
IT - Comunicações

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