Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies
|Title:||Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies||Authors:||Oramas, Sergio
|Permanent link:||http://hdl.handle.net/10197/7975||Date:||11-Aug-2016||Online since:||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.||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Keywords:||Recommender systems||Other versions:||https://wp.nyu.edu/ismir2016/||Language:||en||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|
|Appears in Collections:||Insight Research Collection|
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