Exploring customer reviews for music genre classification and evolutionary studies
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- dc.contributor.author Oramas, Sergioca
- dc.contributor.author Espinosa-Anke, Luisca
- dc.contributor.author Lawlor, Aonghusca
- dc.contributor.author Serra, Xavierca
- dc.contributor.author Saggion, Horacioca
- dc.date.accessioned 2017-10-23T09:14:04Z
- dc.date.available 2017-10-23T09:14:04Z
- dc.date.issued 2016
- dc.description Comunicació presentada a la 17th International Society for Music Information Retrieval Conference (ISMIR 2016), celebrada els dies 7 a 11 d'agost de 2016 a Nova York, EUA.
- dc.description.abstract In this paper, we explore a large multimodal dataset of about 65k albums constructed from a combination of Amazon customer reviews, MusicBrainz metadata and AcousticBrainz audio descriptors. Review texts are further enriched with named entity disambiguation along with polarity information derived from an aspect-based sentiment analysis framework. This dataset constitutes the cornerstone of two main contributions: First, we perform experiments on music genre classification, exploring a variety of feature types, including semantic, sentimental and acoustic features. These experiments show that modeling semantic information contributes to outperforming strong bag-of-words baselines. Second, we provide a diachronic study of the criticism of music genres via a quantitative analysis of the polarity associated to musical aspects over time. Our analysis hints at a potential correlation between key cultural and geopolitical events and the language and evolving sentiments found in music reviews.en
- dc.description.sponsorship This work was partially funded by the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502), by the TUNER project (TIN2015-65308-C5-5-R, MINECO/FEDER, UE), by the Keystone COST Action IC1302 and by the Insight Centre for Data Analytics under grant number SFI/12/RC/2289.
- dc.format.mimetype application/pdfca
- dc.identifier.citation Oramas S, Espinosa-Anke L, Lawlor A, Serra X, Saggion H. Exploring customer reviews for music genre classification and evolutionary studies. In: Devaney J, Mandel MI, Turnbull D, Tzanetakis G, editors. ISMIR 2016. Proceedings of the 17th International Society for Music Information Retrieval Conference; 2016 Aug 7-11; New York City (NY). [Canada]: ISMIR; 2016. p. 150-6.
- dc.identifier.uri http://hdl.handle.net/10230/33063
- dc.language.iso eng
- dc.publisher International Society for Music Information Retrieval (ISMIR)ca
- dc.relation.ispartof Devaney J, Mandel MI, Turnbull D, Tzanetakis G, editors. ISMIR 2016. Proceedings of the 17th International Society for Music Information Retrieval Conference; 2016 Aug 7-11; New York City (NY). [Canada]: ISMIR; 2016. p. 150-6.
- dc.relation.isreferencedby http://hdl.handle.net/10230/34325
- dc.relation.projectID info:eu-repo/grantAgreement/ES/1PE/TIN2015-65308-C5-5-R
- dc.rights © Sergio Oramas, Luis Espinosa-Anke, Aonghus Lawlor, Xavier Serra, Horacio Saggion. Licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0). Attribution: Sergio Oramas, Luis Espinosa-Anke, Aonghus Lawlor, Xavier Serra, Horacio Saggion. “Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies”, 17th International Society for Music Information Retrieval Conference, 2016.
- dc.rights.accessRights info:eu-repo/semantics/openAccess
- dc.rights.uri http://creativecommons.org/licenses/by/4.0/
- dc.subject.other Música -- Anàlisi
- dc.title Exploring customer reviews for music genre classification and evolutionary studiesca
- dc.type info:eu-repo/semantics/conferenceObject
- dc.type.version info:eu-repo/semantics/publishedVersion