The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis

Laura Pollacci; Riccardo Guidotti; Giulio Rossetti; Fosca Giannotti; Dino Pedreschi

Smart Objects and Technologies for Social Good. Third International Conference, GOODTECHS 2017, Pisa, Italy, November 29-30, 2017, Proceedings

Research Article

The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis

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@INPROCEEDINGS{10.1007/978-3-319-76111-4_19,
    author={Laura Pollacci and Riccardo Guidotti and Giulio Rossetti and Fosca Giannotti and Dino Pedreschi},
    title={The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis},
    proceedings={Smart Objects and Technologies for Social Good. Third International Conference, GOODTECHS 2017, Pisa, Italy, November 29-30, 2017, Proceedings},
    proceedings_a={GOODTECHS},
    year={2018},
    month={3},
    keywords={Music data analytics Hierarchical clustering Sentiment pattern discovery Multi-source analytics},
    doi={10.1007/978-3-319-76111-4_19}
}

Laura Pollacci
Riccardo Guidotti
Giulio Rossetti
Fosca Giannotti
Dino Pedreschi
Year: 2018
The Fractal Dimension of Music: Geography, Popularity and Sentiment Analysis
GOODTECHS
Springer
DOI: 10.1007/978-3-319-76111-4_19

Laura Pollacci¹^,*, Riccardo Guidotti²^,*, Giulio Rossetti¹^,*, Fosca Giannotti²^,*, Dino Pedreschi¹^,*

1: University of Pisa
2: ISTI-CNR

*Contact email: laura.pollacci@di.unipi.it, riccardo.guidotti@isti.cnr.it, giulio.rossetti@di.unipi.it, fosca.giannotti@isti.cnr.it, dino.pedreschi@unipi.it

Abstract

Nowadays there is a growing standardization of musical contents. Our finding comes out from a cross-service multi-level dataset analysis where we study how geography affects the music production. The investigation presented in this paper highlights the existence of a “” musical structure that relates the technical characteristics of the music produced at regional, national and world level. Moreover, a similar structure emerges also when we analyze the musicians’ popularity and the polarity of their songs defined as the mood that they are able to convey. Furthermore, the clusters identified are markedly distinct one from another with respect to popularity and sentiment.

Keywords: Music data analytics Hierarchical clustering Sentiment pattern discovery Multi-source analytics

Published: 2018-03-08
Appears in: SpringerLink

: http://dx.doi.org/10.1007/978-3-319-76111-4_19