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dc.contributor.authorBurget, R.
dc.contributor.authorKarasek, J.
dc.contributor.authorSmekal, Z.
dc.date.accessioned2016-02-26T08:17:25Z
dc.date.available2016-02-26T08:17:25Z
dc.date.issued2011-04cs
dc.identifier.citationRadioengineering. 2011, vol. 20, č. 1, s. 39-47. ISSN 1210-2512cs
dc.identifier.issn1210-2512
dc.identifier.urihttp://hdl.handle.net/11012/56796
dc.description.abstractWith the growth of internet community, many different text-based documents are produced. Emotion detection and classification in text becomes very important in human-machine interaction or in human-to-human internet communication with this growth. This article refers to this issue in Czech texts. Headlines were extracted from Czech newspapers and Fear, Joy, Anger, Disgust, Sadness, and Surprise emotions are detected. In this work, several algorithms for learning were assessed and compared according to their accuracy of emotion detection and classification of news headlines. The best results were achieved using the SVM (Support Vector Machine) method with a linear kernel, where the presence of the dominant emotion or emotions was analyzed. For individual emotions the following results were obtained: Anger was detected in 87.3 %, Disgust 95.01%, Fear 81.32 %, Joy 71.6 %, Sadness 75.4 %, and Surprise 71.09 %.en
dc.formattextcs
dc.format.extent39-47cs
dc.format.mimetypeapplication/pdfen
dc.language.isoencs
dc.publisherSpolečnost pro radioelektronické inženýrstvícs
dc.relation.ispartofRadioengineeringcs
dc.relation.urihttp://www.radioeng.cz/fulltexts/2011/11_01_039_047.pdfcs
dc.rightsCreative Commons Attribution 3.0 Unported Licenseen
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/en
dc.subjectEmotion corpusen
dc.subjectEmotion detectionen
dc.subjectEmotion classificationen
dc.subjectText miningen
dc.subjectCzech languageen
dc.titleRecognition of Emotions in Czech Newspaper Headlinesen
eprints.affiliatedInstitution.facultyFakulta eletrotechniky a komunikačních technologiícs
dc.coverage.issue1cs
dc.coverage.volume20cs
dc.rights.accessopenAccessen
dc.type.driverarticleen
dc.type.statusPeer-revieweden
dc.type.versionpublishedVersionen


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