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Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies
| Title | Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies |
| Publication Type | Conference Paper |
| Year of Publication | 2016 |
| Conference Name | 17th International Society for Music Information Retrieval Conference (ISMIR 2016) |
| Authors | Oramas, S. , Espinosa-Anke L. , Lawlor A. , Serra X. , & Saggion H. |
| Pagination | 150-156 |
| Conference Start Date | 07/08/2016 |
| Conference Location | New York |
| 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. |
| preprint/postprint document | http://hdl.handle.net/10230/33063 |
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