Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies

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Title: Exploring Customer Reviews for Music Genre Classification and Evolutionary Studies
Authors: Oramas, Sergio
Espinosa-Anke, Luis
Lawlor, Aonghus
et al.
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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