Advertisement

The italian music superdiversity

Geography, emotion and language: one resource to find them, one resource to rule them all
  • Laura Pollacci
  • Riccardo Guidotti
  • Giulio Rossetti
  • Fosca Giannotti
  • Dino Pedreschi
Article
  • 40 Downloads

Abstract

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.

Keywords

Music data analytics Sentiment pattern discovery Music sentiment analytics Multi-source analytics Music sentiment analysis Superdiversity 

Notes

Acknowledgements

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”.

References

  1. 1.
    Bischoff K, Firan CS, Paiu R, Nejdl W, Laurier C, Sordo M (2009) Music mood and theme classification-a hybrid approach. In: ISMIR, pp 657–662Google Scholar
  2. 2.
    Bradley MM, Lang PJ (1999) Affective norms for english words (anew): instruction manual and affective ratings. Tech. rep., CiteseerGoogle Scholar
  3. 3.
    Çano E., Morisio M (2017) Moodylyrics: a sentiment annotated lyrics dataset. In: Proceedings of the 2017 international conference on intelligent systems, metaheuristics & swarm intelligence. ACM, pp 118–124Google Scholar
  4. 4.
    Çano E., Morisio M (2017) Music mood dataset creation based on last fm tagsGoogle Scholar
  5. 5.
    Celma O (2010) Music recommendation. In: Music recommendation and discovery. Springer, pp 43–85Google Scholar
  6. 6.
    Dodds P S, Danforth C M (2010) Measuring the happiness of large-scale written expression: songs, blogs, and presidents. J Happiness Stud 11(4):441–456CrossRefGoogle Scholar
  7. 7.
    Downie X, Laurier C, Ehmann M (2008) The 2007 mirex audio mood classification task: Lessons learned. In: Proceedings 9th int. Conf. Music inf. retrieval, pp 462–467Google Scholar
  8. 8.
  9. 9.
    Esuli A, Sebastiani F (2007) Sentiwordnet: a high-coverage lexical resource for opinion mining. Evaluation, pp 1–26Google Scholar
  10. 10.
    Guerini M, Gatti L, Turchi M (2013) Sentiment analysis: how to derive prior polarities from sentiwordnet. arXiv:1309.5843
  11. 11.
    Google form service (2018). https://www.google.com/forms/about/
  12. 12.
    Helmholz P, Siemon D, Robra-Bissantz S Summer hot, winter not!–seasonal influences on context-based music recommendationsGoogle Scholar
  13. 13.
    Hu X, Downie JS (2007) Exploring mood metadata: relationships with genre, artist and usage metadata. In: ISMIR, pp 67–72Google Scholar
  14. 14.
    Hu X, Downie JS (2010) When lyrics outperform audio for music mood classification: a feature analysis. In: ISMIR, pp 619–624Google Scholar
  15. 15.
    Hu X, Downie JS, Ehmann AF (2009) Lyric text mining in music mood classification. Am Music 183(5,049):2–209Google Scholar
  16. 16.
    Lamere P, Pampalk E, Schmitz C, Bello J, Chew E, Turnbull D (2008) Social tags and music information retrieval. In: ISMIR, p 24Google Scholar
  17. 17.
    Laurier C, Sordo M, Serra J, Herrera P (2009) Music mood representations from social tags. In: ISMIR, pp 381–386Google Scholar
  18. 18.
    Lee JH, Hu X (2012) Generating ground truth for music mood classification using mechanical turk. In: Proceedings of the 12th ACM/IEEE-CS joint conference on digital libraries. ACM, pp 129–138Google Scholar
  19. 19.
    Li T, Ogihara M (2004) Music artist style identification by semi-supervised learning from both lyrics and content. In: Proceedings of the 12th annual ACM international conference on multimedia. ACM, pp 364–367Google Scholar
  20. 20.
    Lyding V, Stemle E, Borghetti C, Brunello M, Castagnoli S, Dell’Orletta F, Dittmann H, Lenci A, Pirrelli V (2014) The paisa’corpus of italian web texts. In: 9th web as corpus workshop (wac-9)@ EACL 2014, pp 36–43. EACL (European chapter of the association for computational linguistics)Google Scholar
  21. 21.
    Malheiro R, Panda R, Gomes P, Paiva R P (2016) Classification and regression of music lyrics: Emotionally-significant features. In: 8th international conference on knowledge discovery and information retrievalGoogle Scholar
  22. 22.
    Mihalcea R, Strapparava C (2012) Lyrics, music, and emotions. In: Proceedings of the 2012 joint conference on empirical methods in natural language processing and computational natural language learning. Association for Computational Linguistics, pp 590–599Google Scholar
  23. 23.
    Perna S, Guarasci R, Maisto A, Vitale P (2016) Il linguaggio del rap. possibilità di un’analisi multidisciplinare. In: Editrice A. (ed) XXVI Convegno internazionale ass.i.term. Terminologia e organizzazione della conoscenza nella conservazione della memoria digitale, vol 34. AIDAinformazioni, Rende (CS), pp 209–217Google Scholar
  24. 24.
    PODIUC RE, GRATIE D, VOICU O Inferring song moods from lyricsGoogle Scholar
  25. 25.
    Pollacci L, Guidotti R, Rossetti G (2016) Are we playing like music-stars? Placing emerging artists on the Italian music scene. In: 9th international workshop on machine learning and musicGoogle Scholar
  26. 26.
    Pollacci L, Guidotti R, Rossetti G, Giannotti F, Pedreschi D (2017) The fractal dimension of music: geography, popularity and sentiment analysis. In: International conference on smart objects and technologies for social good, pp 183–194. SpringerGoogle Scholar
  27. 27.
    Pollacci L, Sîrbu A, Giannotti F, Pedreschi D, Lucchese C, Muntean CI (2017) Sentiment spreading: an epidemic model for lexicon-based sentiment analysis on twitter. In: Conference of the Italian association for artificial intelligence. Springer, pp 114–127Google Scholar
  28. 28.
    Rawlings D, Ciancarelli V (1997) Music preference and the five-factor model of the neo personality inventory. Psychol Music 25(2):120–132CrossRefGoogle Scholar
  29. 29.
    Rentfrow PJ, Gosling SD (2003) The do re mi’s of everyday life: the structure and personality correlates of music preferences. J Pers Soc Psychol 84(6):1236CrossRefGoogle Scholar
  30. 30.
    Russell JA (1980) A circumplex model of affect. J Pers Soc Psychol 39(6):1161CrossRefGoogle Scholar
  31. 31.
    Schedl M, Orio N, Liem C, Peeters G (2013) A professionally annotated and enriched multimodal data set on popular music. In: Proceedings of the 4th ACM multimedia systems conference. ACM, pp 78–83Google Scholar
  32. 32.
    Schmid H (1995) Improvements in part-of-speech tagging with an application to German. In: Proceedings of the acl sigdat-workshop. CiteseerGoogle Scholar
  33. 33.
    Schmid H (2013) Probabilistic part-ofispeech tagging using decision trees. In: New methods in language processing, p 154Google Scholar
  34. 34.
  35. 35.
    Spotify (2018). https://www.spotify.com/
  36. 36.
  37. 37.
    Toscana100band contest (2018). http://toscana100band.it/
  38. 38.
    Trohidis K, Tsoumakas G, Kalliris G, Vlahavas IP (2008) Multi-label classification of music into emotions. In: ISMIR, vol 8, pp 325–330Google Scholar
  39. 39.
    Turnbull D, Barrington L, Torres D, Lanckriet G (2008) Semantic annotation and retrieval of music and sound effects. IEEE Trans Audio Speech Lang Process 16(2):467–476CrossRefGoogle Scholar
  40. 40.
    Vertovec S (2006) The emergence of super-diversity in Britain. Centre of Migration, Policy and Society University of OxfordGoogle Scholar
  41. 41.
    Vertovec S (2007) Super-diversity and its implications. Ethn Racial Stud 30(6):1024–1054CrossRefGoogle Scholar
  42. 42.
    Wikipedia - ita version (2018). https://it.wikipedia.org/wiki/Pagina_principale

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Computer ScienceUniversity of PisaPisaItaly
  2. 2.ISTI-CNRPisaItaly

Personalised recommendations