Please use this identifier to cite or link to this item: https://hdl.handle.net/10316/95163
DC FieldValueLanguage
dc.contributor.authorMalheiro, Ricardo-
dc.contributor.authorPanda, Renato-
dc.contributor.authorGomes, Paulo J. S.-
dc.contributor.authorPaiva, Rui Pedro-
dc.date.accessioned2021-07-04T17:35:44Z-
dc.date.available2021-07-04T17:35:44Z-
dc.date.issued2016-
dc.identifier.isbn978-989-758-203-5-
dc.identifier.issn2184-3228-
dc.identifier.urihttps://hdl.handle.net/10316/95163-
dc.description.abstractThis research addresses the role of lyrics in the music emotion recognition process. Our approach is based on several state of the art features complemented by novel stylistic, structural and semantic features. To evaluate our approach, we created a ground truth dataset containing 180 song lyrics, according to Russell's emotion model. We conduct four types of experiments: regression and classification by quadrant, arousal and valence categories. Comparing to the state of the art features (ngrams-baseline), adding other features, including novel features, improved the F-measure from 68.2%, 79.6% and 84.2% to 77.1%, 86.3% and 89.2%, respectively for the three classification experiments. To study the relation between features and emotions (quadrants) we performed experiments to identify the best features that allow to describe and discriminate between arousal hemispheres and valence meridians. To further validate these experiments, we built a validation set comprising 771 lyrics extracted from the AllMusic platform, having achieved 73.6% Fmeasure in the classification by quadrants. Regarding regression, results show that, comparing to similar studies for audio, we achieve a similar performance for arousal and a much better performance for valence.pt
dc.description.sponsorshipThis work was supported by CISUC (Center for Informatics and Systems of the University of Coimbra).pt
dc.language.isoengpt
dc.publisherSciTePresspt
dc.rightsopenAccesspt
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/pt
dc.subjectlyrics feature extractionpt
dc.subjectlyrics music classificationpt
dc.subjectlyrics music emotion recognitionpt
dc.subjectlyrics music regressionpt
dc.subjectmusic information eetrievalpt
dc.titleClassification and Regression of Music Lyrics: Emotionally-Significant Featurespt
dc.typeconferenceObjectpt
degois.publication.locationPorto, Portugalpt
degois.publication.title8th International Conference on Knowledge Discovery and Information Retrieval – KDIR 2016pt
dc.relation.publisherversionhttps://www.scitepress.org/Link.aspx?doi=10.5220/0006037400450055pt
dc.peerreviewedyespt
dc.identifier.doi10.5220/0006037400450055-
dc.date.embargo2016-01-01*
uc.date.periodoEmbargo0pt
item.openairetypeconferenceObject-
item.languageiso639-1en-
item.openairecristypehttp://purl.org/coar/resource_type/c_18cf-
item.cerifentitytypePublications-
item.grantfulltextopen-
item.fulltextCom Texto completo-
crisitem.author.deptFaculty of Sciences and Technology-
crisitem.author.deptFaculty of Sciences and Technology-
crisitem.author.parentdeptUniversity of Coimbra-
crisitem.author.parentdeptUniversity of Coimbra-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.researchunitCISUC - Centre for Informatics and Systems of the University of Coimbra-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.parentresearchunitFaculty of Sciences and Technology-
crisitem.author.orcid0000-0002-3010-2732-
crisitem.author.orcid0000-0003-2539-5590-
crisitem.author.orcid0000-0003-3215-3960-
Appears in Collections:I&D CISUC - Artigos em Livros de Actas
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