Contributo in atti di convegno, 2018, ENG, 10.1007/978-3-319-76111-4_19
Pollacci L.; Rossetti G.; Guidotti R.; Giannotti F.; Pedreschi D.
Department of Computer Science, University of Pisa, Pisa, Italy; CNR-ISTI, Pisa, Italy; Department of Computer Science, University of Pisa - CNR-ISTI, Pisa, Italy; CNR-ISTI, Pisa, Italy; Department of Computer Science, University of Pisa - CNR-ISTI, Pisa, Italy
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.
GOODTECHS 2017 - Third International Conference on Smart Objects and Technologies for Social Good, pp. 183–194, Pisa, Italy, 29-30 November 2017
Music data analytics, Hierarchical clustering, Sentiment pattern discovery, Multi-source analytics
Pedreschi Dino, Guidotti Riccardo, Giannotti Fosca, Rossetti Giulio
ISTI – Istituto di scienza e tecnologie dell'informazione "Alessandro Faedo"
ID: 384754
Year: 2018
Type: Contributo in atti di convegno
Creation: 2018-03-06 14:55:20.000
Last update: 2019-07-17 14:44:33.000
External links
OAI-PMH: Dublin Core
OAI-PMH: Mods
OAI-PMH: RDF
DOI: 10.1007/978-3-319-76111-4_19
URL: https://link.springer.com/chapter/10.1007/978-3-319-76111-4_19
External IDs
CNR OAI-PMH: oai:it.cnr:prodotti:384754
DOI: 10.1007/978-3-319-76111-4_19
Scopus: 2-s2.0-85043585685