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.
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Notes
All Music Guide website: http://www.allmusic.com
The Amazon Mechanical Turk (MTurk) is a web server for works that require human intelligence. Developers can exploit the service to build human intelligence directly into their applications. https://www.mturk.com/.
Last.fm website: https://www.last.fm/
Discogs website: https://www.discogs.com/
AIDAinformazioni, Anno 34, numero 1-2, 2016: http://www.aidainformazioni.it/wp-content/archivio/anno34_n1_2_2016/perna%20et%20al.pdf
2007 Audio Music Mood Classification: http://www.music-ir.org/mirex/wiki/2007:Audio_Music_Mood_Classification
The service is now inactive, with the URL resulting in a 404 response.
The two version of the WDYL’s list can be public downloaded from https://www.freewebheaders.com/full-list-of-bad-words-banned-by-google/
Googletrans is a free and unlimited python library that implemented Google Translate API. For more details see http://py-googletrans.readthedocs.io/en/latest/
Goslate provides a python API to Google translation service by querying google translation website. More details can be found at https://pythonhosted.org/goslate/
“Crooner” is an American term given to male singers of jazz standards, accompanied by either a full orchestra, a big band or a piano. The most famous America crooner is Frank Sinatra, despite the fact that he does not consider himself a crooner.
For example, the dictionary we obtained for Lazio for a selected run with best parameters (the same showed in Fig. 9a) is composed of 11,243 Italian lemmas, each labeled with a polarity score in the range [0,10].
References
Bischoff K, Firan CS, Paiu R, Nejdl W, Laurier C, Sordo M (2009) Music mood and theme classification-a hybrid approach. In: ISMIR, pp 657–662
Bradley MM, Lang PJ (1999) Affective norms for english words (anew): instruction manual and affective ratings. Tech. rep., Citeseer
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 118–124
Çano E., Morisio M (2017) Music mood dataset creation based on last fm tags
Celma O (2010) Music recommendation. In: Music recommendation and discovery. Springer, pp 43–85
Dodds P S, Danforth C M (2010) Measuring the happiness of large-scale written expression: songs, blogs, and presidents. J Happiness Stud 11(4):441–456
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 462–467
Echonest web api (2018). http://docs.echonest.com.s3-website-us-east-1.amazonaws.com/
Esuli A, Sebastiani F (2007) Sentiwordnet: a high-coverage lexical resource for opinion mining. Evaluation, pp 1–26
Guerini M, Gatti L, Turchi M (2013) Sentiment analysis: how to derive prior polarities from sentiwordnet. arXiv:1309.5843
Google form service (2018). https://www.google.com/forms/about/
Helmholz P, Siemon D, Robra-Bissantz S Summer hot, winter not!–seasonal influences on context-based music recommendations
Hu X, Downie JS (2007) Exploring mood metadata: relationships with genre, artist and usage metadata. In: ISMIR, pp 67–72
Hu X, Downie JS (2010) When lyrics outperform audio for music mood classification: a feature analysis. In: ISMIR, pp 619–624
Hu X, Downie JS, Ehmann AF (2009) Lyric text mining in music mood classification. Am Music 183(5,049):2–209
Lamere P, Pampalk E, Schmitz C, Bello J, Chew E, Turnbull D (2008) Social tags and music information retrieval. In: ISMIR, p 24
Laurier C, Sordo M, Serra J, Herrera P (2009) Music mood representations from social tags. In: ISMIR, pp 381–386
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 129–138
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 364–367
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)
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 retrieval
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 590–599
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 209–217
PODIUC RE, GRATIE D, VOICU O Inferring song moods from lyrics
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 music
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. Springer
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 114–127
Rawlings D, Ciancarelli V (1997) Music preference and the five-factor model of the neo personality inventory. Psychol Music 25(2):120–132
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):1236
Russell JA (1980) A circumplex model of affect. J Pers Soc Psychol 39(6):1161
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 78–83
Schmid H (1995) Improvements in part-of-speech tagging with an application to German. In: Proceedings of the acl sigdat-workshop. Citeseer
Schmid H (2013) Probabilistic part-ofispeech tagging using decision trees. In: New methods in language processing, p 154
Soundcloud web api (2018). https://developers.soundcloud.com/docs/api/guide
Spotify (2018). https://www.spotify.com/
Spotify web api (2018). https://developer.spotify.com/web-api/
Toscana100band contest (2018). http://toscana100band.it/
Trohidis K, Tsoumakas G, Kalliris G, Vlahavas IP (2008) Multi-label classification of music into emotions. In: ISMIR, vol 8, pp 325–330
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):467–476
Vertovec S (2006) The emergence of super-diversity in Britain. Centre of Migration, Policy and Society University of Oxford
Vertovec S (2007) Super-diversity and its implications. Ethn Racial Stud 30(6):1024–1054
Wikipedia - ita version (2018). https://it.wikipedia.org/wiki/Pagina_principale
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”.
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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
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DOI: https://doi.org/10.1007/s11042-018-6511-6