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Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies
Date Issued
2016-08-11
Date Available
2016-09-20T12:26:20Z
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.
Sponsorship
Science Foundation Ireland
Type of Material
Conference Publication
Keywords
Web versions
Language
English
Status of Item
Peer reviewed
Conference Details
The 17th International Society for Music Information Retrieval Conference (ISMIR 2016), New York City, United States of America, 7-11 August 2016
This item is made available under a Creative Commons License
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