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dc.contributor.authorMusil, David
dc.date.accessioned2018-07-10T12:48:13Z
dc.date.available2018-07-10T12:48:13Z
dc.date.issued2016cs
dc.identifier.citationProceedings of the 22nd Conference STUDENT EEICT 2016. s. 161-163. ISBN 978-80-214-5350-0cs
dc.identifier.isbn978-80-214-5350-0
dc.identifier.urihttp://hdl.handle.net/11012/83904
dc.description.abstractIn the present, obtaining and sorting knowledge from data produced by various sources requires significant effort which is not ensured easily by a human, meaning machine processing is taking place. Purpose of this work was to create a system capable of positive and negative emotion detection from text along with evaluation of its performance. System allows training with use of large amount of data (known as Big Data), exploiting Spark library. Classificator model was created with use of Support Vector Machines. Highest achieved accuracy is 78,05% for Czech, 79,73% for German and 91,88% for English.en
dc.formattextcs
dc.format.extent161-163cs
dc.format.mimetypeapplication/pdfen
dc.language.isocscs
dc.publisherVysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.relation.ispartofProceedings of the 22nd Conference STUDENT EEICT 2016en
dc.relation.urihttp://www.feec.vutbr.cz/EEICT/cs
dc.rights© Vysoké učení technické v Brně, Fakulta elektrotechniky a komunikačních technologiícs
dc.subjectartificial intelligenceen
dc.subjectBig Dataen
dc.subjectemotion detectionen
dc.subjecttext-miningen
dc.titleAlgorithm for Detection of Positive and Negative Texten
eprints.affiliatedInstitution.departmentFakulta elektrotechniky a komunikačních technologiícs
but.event.date28.04.2016cs
but.event.titleStudent EEICT 2016cs
dc.rights.accessopenAccessen
dc.type.driverconferenceObjecten
dc.type.statusPeer-revieweden
dc.type.versionPublishers's versionen


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