About: Recognition of Emotions in Czech Newspaper Headlines

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An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

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rdf:type	skos:Concept http://linked.opendata.cz/ontology/domain/vavai/Vysledek
Description	With 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 %. With 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)
Title	Recognition of Emotions in Czech Newspaper Headlines Recognition of Emotions in Czech Newspaper Headlines (en)
skos:prefLabel	Recognition of Emotions in Czech Newspaper Headlines Recognition of Emotions in Czech Newspaper Headlines (en)
skos:notation	RIV/00216305:26220/11:PU91508!RIV12-MSM-26220___
http://linked.open...avai/riv/aktivita	P Z
http://linked.open...avai/riv/aktivity	P(FR-TI2/679), Z(MSM0021630513)
http://linked.open...iv/cisloPeriodika	1
http://linked.open...vai/riv/dodaniDat	2012
http://linked.open...aciTvurceVysledku	Karásek, Jan Burget, Radim Smékal, Zdeněk
http://linked.open.../riv/druhVysledku	J - Článek v odborném periodiku
http://linked.open...iv/duvernostUdaju	S - Úplné a pravdivé údaje nepodléhající ochraně podle zvláštních právních předpisů
http://linked.open...titaPredkladatele	Vysoké učení technické v Brně / Fakulta elektrotechniky a komunikačních technologií
http://linked.open...dnocenehoVysledku	225952
http://linked.open...ai/riv/idVysledku	RIV/00216305:26220/11:PU91508
http://linked.open...riv/jazykVysledku	eng - angličtina
http://linked.open.../riv/klicovaSlova	Emotion corpus, Emotion detection, Emotion classification, Text mining, Czech, artificial intelligence (en)
http://linked.open.../riv/klicoveSlovo	Czech artificial intelligence Text mining Emotion classification Emotion corpus Emotion detection
http://linked.open...odStatuVydavatele	CZ - Česká republika
http://linked.open...ontrolniKodProRIV	[2E3C6F8E7D2D]
http://linked.open...i/riv/nazevZdroje	Radioengineering
http://linked.open...in/vavai/riv/obor	BD
http://linked.open...ichTvurcuVysledku	3 (xsd:int)
http://linked.open...cetTvurcuVysledku	3 (xsd:int)
http://linked.open...vavai/riv/projekt	*Media-informatics system supporting advanced multimedia services
http://linked.open...UplatneniVysledku	2011
http://linked.open...v/svazekPeriodika	2011
http://linked.open...iv/tvurceVysledku	Burget, Radim Karásek, Jan Smékal, Zdeněk
http://linked.open...n/vavai/riv/zamer	Advanced Electronic Communication Systems and Technologies (ELCOM)
issn	1210-2512
number of pages	9 (xsd:int)
http://localhost/t...ganizacniJednotka	26220

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