Classification and regression of music lyrics: Emotionally-significant features

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dc.contributor.authorMalheiro, Ricardo
dc.contributor.authorPanda, Renato
dc.contributor.authorGomes, Paulo
dc.contributor.authorPaiva, Rui Pedro
dc.date.accessioned2017-10-13T14:22:19Z
dc.date.available2017-10-13T14:22:19Z
dc.date.issued2016-01
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% F-measure 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_PT
dc.identifier.urihttp://repositorio.ismt.pt/handle/123456789/717
dc.language.isoenpt_PT
dc.publisher8th International Conference on Knowledge Discovery and Information Retrievalpt_PT
dc.subjectMusic Information Retrievalpt_PT
dc.subjectLyrics Music Emotion Recognitionpt_PT
dc.subjectLyrics Music Classificationpt_PT
dc.subjectLyrics Music Regressionpt_PT
dc.subjectLyrics Feature Extractionpt_PT
dc.titleClassification and regression of music lyrics: Emotionally-significant featurespt_PT
dc.typePresentationpt_PT
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