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Title: Classification and Regression of Music Lyrics: Emotionally-Significant Features
Authors: Malheiro, Ricardo 
Panda, Renato 
Gomes, Paulo J. S. 
Paiva, Rui Pedro 
Keywords: lyrics feature extraction; lyrics music classification; lyrics music emotion recognition; lyrics music regression; music information eetrieval
Issue Date: 2016
Publisher: SciTePress
Serial title, monograph or event: 8th International Conference on Knowledge Discovery and Information Retrieval – KDIR 2016
Place of publication or event: Porto, Portugal
Abstract: This 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.
ISBN: 978-989-758-203-5
ISSN: 2184-3228
DOI: 10.5220/0006037400450055
Rights: openAccess
Appears in Collections:I&D CISUC - Artigos em Livros de Actas

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