%0 Conference Proceedings
%T Mood Classification Using Lyrics and Audio: A Case-Study in Greek Music
%+ Department of Informatics [Ionian University]
%A Brilis, Spyros
%A Gkatzou, Evagelia
%A Koursoumis, Antonis
%A Talvis, Karolos
%A Kermanidis, Katia, L.
%A Karydis, Ioannis
%Z Part 7: First Mining Humanistic Data Workshop (MHDW 2012)
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 8th International Conference on Artificial Intelligence Applications and Innovations (AIAI)
%C Halkidiki, Greece
%Y Lazaros Iliadis
%Y Ilias Maglogiannis
%Y Harris Papadopoulos
%Y Kostas Karatzas
%Y Spyros Sioutas
%I Springer
%3 Artificial Intelligence Applications and Innovations
%V AICT-382
%N Part II
%P 421-430
%8 2012-09-27
%D 2012
%R 10.1007/978-3-642-33412-2_43
%K music mood classification
%K lyrics
%K audio
%K Greek music
%Z Computer Science [cs]Conference papers
%X This paper presents a case-study of the effectiveness of a trained system into classifying Greek songs according to their audio characteristics or/and their lyrics into moods. We examine how the usage of different algorithms, featureset combinations and pre-processing parameters affect the precision and recall percentages of the classification process for each mood model characteristic. Experimental results indicate that the current selection of features offers accuracy results, the superiority of lyrics content over generic audio features as well as potential caveats with current research in Greek language stemming pre-processing methods.
%G English
%Z TC 12
%Z WG 12.5
%2 https://hal.science/hal-01523081/document
%2 https://hal.science/hal-01523081/file/978-3-642-33412-2_43_Chapter.pdf
%L hal-01523081
%U https://hal.science/hal-01523081
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-AIAI
%~ IFIP-WG12-5
%~ IFIP-AICT-382