Globalization can lead to a growing standardization of musical contents. Using a cross-service multi-level dataset we investigate the actual Italian music scene. The investigation highlights the musical Italian superdiversity both individually analyzing the geographical and lexical dimensions and combining them. Using different kinds of features over the geographical dimension leads to two similar, comparable and coherent results, confirming the strong and essential correlation between melodies and lyrics. The profiles identified are markedly distinct one from another with respect to sentiment, lexicon, and melodic features. Through a novel application of a sentiment spreading algorithm and songs’ melodic features, we are able to highlight discriminant characteristics that violate the standard regional political boundaries, reconfiguring them following the actual musical communicative practices.
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This work is supported by the European Community’s H2020 Program under the funding scheme “INFRAIA-1-2014-2015: Research Infrastructures” grant agreement, http://www.sobigdata.eu, GS501100001809, 654024 “SoBigData: Social Mining & Big Data Ecosystem”.
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Pollacci, L., Guidotti, R., Rossetti, G. et al. The italian music superdiversity. Multimed Tools Appl 78, 3297–3319 (2019). https://doi.org/10.1007/s11042-018-6511-6
- Music data analytics
- Sentiment pattern discovery
- Music sentiment analytics
- Multi-source analytics
- Music sentiment analysis