NDLI: Exploring the relationship between categorical and dimensional emotion semantics of music

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Exploring the relationship between categorical and dimensional emotion semantics of music

Content Provider	ACM Digital Library
Author	Chang, Kaichun Wang, Hsin-Min Wang, Ju-Chiang Yang, Yi-Hsuan Jeng, Shyh-Kang
Abstract	Computational modeling of music emotion has been addressed primarily by two approaches: the categorical approach that categorizes emotions into mood classes and the dimensional approach that regards emotions as numerical values over a few dimensions such as valence and activation. Being two extreme scenarios (discrete/continuous), the two approaches actually share a unified goal of understanding the emotion semantics of music. This paper presents the first computational model that unifies the two semantic modalities under a probabilistic framework, which makes it possible to explore the relationship between them in a computational way. With the proposed framework, mood labels can be mapped into the emotion space in an unsupervised and content-based manner, without any training ground truth annotations for the semantic mapping. Such a function can be applied to automatically generate a semantically structured tag cloud in the emotion space. To demonstrate the effectiveness of the proposed framework, we qualitatively evaluate the mood tag clouds generated from two emotion-annotated corpora, and quantitatively evaluate the accuracy of the categorical-dimensional mapping by comparing the results with those created by psychologists, including the one proposed by Whissell & Plutchik and the one defined in the Affective Norms for English Words (ANEW).
Starting Page	63
Ending Page	68
Page Count	6
File Format	PDF
ISBN	9781450315913
DOI	10.1145/2390848.2390865
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2012-11-02
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Multi-modal emotion semantic modeling Computational music emotion analysis Automatic layout of tag cloud
Content Type	Text
Resource Type	Article

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