Advertisement

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

  • Laura Pollacci
  • Riccardo Guidotti
  • Giulio Rossetti
  • Fosca Giannotti
  • Dino Pedreschi
Conference paper
Part of the Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering book series (LNICST, volume 233)

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 “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’ 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 

Notes

Acknowledgment

This work is partially supported by the European Project SoBigData: Social Mining & Big Data Ecosystem, http://www.sobigdata.eu. This work is partially supported by the European Communitys 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.
    Spotify. https://www.spotify.com/. Accessed 28 July 2017
  2. 2.
    Toscana100band contest. http://toscana100band.it/. Accessed 28 July 2017
  3. 3.
    Spotify Web API. https://developer.spotify.com/web-api/. Accessed 28 July 2017
  4. 4.
    Genius. https://genius.com/. Accessed 28 July 2017
  5. 5.
    SoundCloud API. https://developers.soundcloud.com/docs/api/guide. Accessed 28 July 2017
  6. 6.
    Google Form service. https://www.google.com/forms/about/. Accessed 28 July 2017
  7. 7.
    Spotify Audio Features Object. https://developer.spotify.com/web-api/get-several-audio-features/. Accessed 28 July 2017
  8. 8.
    AllMusic. http://www.allmusic.com/genres. Accessed 28 July 2017
  9. 9.
    Goslate. http://pythonhosted.org/goslate/. Accessed 28 July 2017
  10. 10.
    List of popular music genres, (n.d.). Wikipedia. http://en.wikipedia.org/wiki/Psychology. Accessed 28 July 2017
  11. 11.
    Clarke, F., Ekeland, I.: Nonlinear oscillations and boundary-value problems for Hamiltonian systems. Arch. Rat. Mech. Anal. 78, 315–333 (1982)MathSciNetCrossRefzbMATHGoogle Scholar
  12. 12.
    Clarke, F., Ekeland, I.: Solutions périodiques, du période donnée, des équations hamiltoniennes. Note CRAS Paris 287, 1013–1015 (1978)zbMATHGoogle Scholar
  13. 13.
    Michalek, R., Tarantello, G.: Subharmonic solutions with prescribed minimal period for nonautonomous Hamiltonian systems. J. Differ. Equ. 72, 28–55 (1988)MathSciNetCrossRefzbMATHGoogle Scholar
  14. 14.
    Tarantello, G.: Subharmonic solutions for Hamiltonian systems via a \(Z\!\!Z_p\) pseudoindex theory. Annali di Matematica Pura (to appear)Google Scholar
  15. 15.
    Rabinowitz, P.: On subharmonic solutions of a Hamiltonian system. Commun. Pure Appl. Math. 33, 609–633 (1980)MathSciNetCrossRefzbMATHGoogle Scholar
  16. 16.
    Guidotti, R., Rossetti, G., Pedreschi, D.: Audio ergo sum. In: Milazzo, P., Varró, D., Wimmer, M. (eds.) STAF 2016. LNCS, vol. 9946, pp. 51–66. Springer, Cham (2016).  https://doi.org/10.1007/978-3-319-50230-4_5 CrossRefGoogle Scholar
  17. 17.
    Rawlings, D., Ciancarelli, V.: Music preference and the five-factor model of the NEO personality inventory. Psychol. Music 25(2), 120–132 (1997)CrossRefGoogle Scholar
  18. 18.
    Rentfrow, P.J., Gosling, S.D.: The do re mi’s of everyday life: the structure and personality correlates of music preferences. J. Pers. Soc. Psychol. 84(6), 1236 (2003)CrossRefGoogle Scholar
  19. 19.
    Bu, J., Tan, S., Chen, C., Wang, C., Wu, H., Zhang, L., He, X.: Music recommendation by unified hypergraph: combining social media information and music content. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 391–400. ACM (2010)Google Scholar
  20. 20.
    Bischoff, K.: We love rock‘n’roll: analyzing and predicting friendship links in last. fm. In: Proceedings of the 4th Annual ACM Web Science Conference, pp. 47–56. ACM (2012)Google Scholar
  21. 21.
    Pennacchioli, D., Rossetti, G., Pappalardo, L., Pedreschi, D., Giannotti, F., Coscia, M.: The three dimensions of social prominence. In: Jatowt, A., Lim, E.-P., Ding, Y., Miura, A., Tezuka, T., Dias, G., Tanaka, K., Flanagin, A., Dai, B.T. (eds.) SocInfo 2013. LNCS, vol. 8238, pp. 319–332. Springer, Cham (2013).  https://doi.org/10.1007/978-3-319-03260-3_28 CrossRefGoogle Scholar
  22. 22.
    Putzke, J., Fischbach, K., Schoder, D., Gloor, P.A.: Cross-cultural gender differences in the adoption and usage of social media platforms - an exploratory study of Last.FM. Comput. Netw. 75, 519–530 (2014). http://dx.doi.org/10.1016/j.comnet.2014.08.027 CrossRefGoogle Scholar
  23. 23.
    Park, M., Weber, I., Naaman, M., Vieweg, S.: Understanding musical diversity via online social media. In: Ninth International AAAI Conference on Web and Social Media (2015)Google Scholar
  24. 24.
    Zheleva, E., Guiver, J., Mendes Rodrigues, E., Milić-Frayling, N.: Statistical models of music-listening sessions in social media. In: Proceedings of the 19th International Conference on World wide web, pp. 1019–1028. ACM (2010)Google Scholar
  25. 25.
    Li, T., Ogihara, M., Peng, W., Shao, B., Zhu, S.: Music clustering with features from different information sources. IEEE Trans. Multimed. 11(3), 477–485 (2009)CrossRefGoogle Scholar
  26. 26.
    Peng, W., Li, T., Ogihara, M.: Music clustering with constraints. In: ISMIR, pp. 27–32 (2007)Google Scholar
  27. 27.
    González-Pardo, A., Granados, A., Camacho, D., de Borja Rodríguez, F.: Influence of music representation on compression-based clustering. In: 2010 IEEE Congress on Evolutionary Computation (CEC), pp. 1–8. IEEE (2010)Google Scholar
  28. 28.
    Schmid, H.: Improvements in part-of-speech tagging with an application to German. In: Proceedings of the ACL SIGDAT-workshop. Citeseer (1995)Google Scholar
  29. 29.
    Schmid, H.: Part-of-speech tagging with neural networks. In: Proceedings of the 15th Conference on Computational Linguistics, vol. 1, pp. 172–176. Association for Computational Linguistics (1994)Google Scholar
  30. 30.
    Tan, P.-N., Steinbach, M., Kumar, V., et al.: Introduction to Data Mining, vol. 1. Pearson Addison Wesley, Boston (2006)Google Scholar
  31. 31.
    Pollacci, L., Guidotti, R., Rossetti, G.: Are we playing like Music-Stars? Placing emerging artists on the Italian music scene (2016)Google Scholar
  32. 32.
    Bradley, M.M., Lang, P.J.: Affective norms for English words (ANEW): instruction manual and affective ratings. Citeseer, Technical report (1999)Google Scholar
  33. 33.
    Lang, P.J.: Behavioral treatment and bio-behavioral assessment: computer applications (1980)Google Scholar
  34. 34.
    Dodds, P.S., Danforth, C.M.: Measuring the happiness of large-scale written expression: songs, blogs, and presidents. J. Happiness Stud. 11(4), 441–456 (2010)CrossRefGoogle Scholar
  35. 35.
    Verhulst, P.F.: Recherches mathématiques sur la loi d’accroissement de la population. Nouveaux mémoires de l’académie royale des sciences et belles-lettres de Bruxelles 18, 14–54 (1845)Google Scholar

Copyright information

© ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2018

Authors and Affiliations

  • Laura Pollacci
    • 1
  • Riccardo Guidotti
    • 2
  • Giulio Rossetti
    • 1
  • Fosca Giannotti
    • 2
  • Dino Pedreschi
    • 1
  1. 1.Department of Computer ScienceUniversity of PisaPisaItaly
  2. 2.ISTI-CNRPisaItaly

Personalised recommendations