Nowadays there is a growing standardization of musical con- tents. 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 "fractal" 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' popular- ity 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.

The fractal dimension of music: geography, popularity and sentiment analysis

Rossetti G;Guidotti R;Giannotti F;Pedreschi D
2018

Abstract

Nowadays there is a growing standardization of musical con- tents. 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 "fractal" 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' popular- ity 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.
2018
Istituto di Scienza e Tecnologie dell'Informazione "Alessandro Faedo" - ISTI
Inglese
Barbara Guidi, Laura Ricci, Carlos Calafate, Ombretta Gaggi, Johann Marquez-Barja
Smart Objects and Technologies for Social Good
GOODTECHS 2017 - Third International Conference on Smart Objects and Technologies for Social Good
183
194
978-3-319-76110-7
https://link.springer.com/chapter/10.1007/978-3-319-76111-4_19
Sì, ma tipo non specificato
29-30 November 2017
Pisa, Italy
Music data analytics
Hierarchical clustering
Sentiment pattern discovery
Multi-source analytics
5
partially_open
Pollacci, L; Rossetti, G; Guidotti, R; Giannotti, F; Pedreschi, D
273
info:eu-repo/semantics/conferenceObject
04 Contributo in convegno::04.01 Contributo in Atti di convegno
   SoBigData Research Infrastructure
   SoBigData
   H2020
   654024
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/346801
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