The times they are a-changin' (or not): song lyrics analysis over the years
Authors: 
Carolina C. Vieira, Túlio Corrêa Loures, Pedro O. S. Vaz de Melo, Renato Martins AssunçãoAuthors Info & Claims
Pages 237 - 244
Abstract
With the growth of music streaming services, the demand for organizing songs according to similarity has increased. In this way, this work proposes to study lyrics of popular songs in order to identify the changes in the vocabulary of the compositions. Analyzing the text, we look for evidences that the themes portrayed by the lyrics undergo changes over the years. When the words used in the compositions do not discriminate years or decades, the context in which those words are inserted causes their meaning to change. Thus, analyzing when certain words occur and how different contexts change their meanings, it is possible to characterize song lyrics temporally. In the future, this analysis can facilitate temporal classification tasks of song lyrics, which can be extended to other types of documents.
References
[1]
Carlos V.S. Araujo, Rayol M. Neto, Fabiola G. Nakamura, and Eduardo F. Nakamura. 2017. Predicting Music Success Based on Users' Comments on Online Social Networks. In Proceedings of the 23rd Brazillian Symposium on Multimedia and the Web (WebMedia '17). ACM, 149--156.
[2]
Carlos V. S. Araujo and Eduardo F. Nakamura. 2018. Identification of Most Popular Musical Genres and Their Influence Factors. In Proceedings of the 24th Brazilian Symposium on Multimedia and the Web (WebMedia '18). ACM, 233--236.
[3]
Argyris Argyrou, Stamatios Giannoulakis, and Nicolas Tsapatsoulis. 2018. Topic modelling on Instagram hashtags: An alternative way to Automatic Image Annotation?. In 2018 13th International Workshop on Semantic and Social Media Adaptation and Personalization (SMAP). IEEE, IEEE, 61--67.
[4]
Robert Bamler and Stephan Mandt. 2017. Dynamic Word Embeddings. In Proceedings of the 34th International Conference on Machine Learning - Volume 70 (ICML'17). JMLR.org, Sydney, NSW, Australia, 380--389. http://dl.acm.org/citation.cfm?id=3305381.3305421
[5]
Charlotte Olivia Brand, Alberto Acerbi, and Alex Mesoudi. 2019. Cultural evolution of emotional expression in 50 years of song lyrics. SocArXiv (2019), 1--14.
[6]
Peter G. Christenson, Silvia de Haan-Rietdijk, Donald F. Roberts, and Tom F.M. ter Bogt. 2018. What has America been singing about? Trends in themes in the U.S. top-40 songs: 1960--2010. Psychology of Music 0, 0 (2018), 0305735617748205.
[7]
The Nielsen Company. 2011. Music sales measurement. Technical Report. The Nielsen Company. http://www.nielsen.com/content/dam/corporate/us/en/docs/solutions/measurement/radio/Soundscan-US-Sales-Procedure.pdf
[8]
C Nathan DeWall, Richard S Pond Jr, W Keith Campbell, and Jean M Twenge. 2011. Tuning in to psychological change: Linguistic markers of psychological traits and emotions over time in popular US song lyrics. Psychology of Aesthetics, Creativity, and the Arts 5, 3 (2011), 200.
[9]
Richard L Dukes, Tara M Bisel, Karoline N Borega, Eligio A Lobato, and Matthew D Owens. 2003. Expressions of love, sex, and hurt in popular songs: A content analysis of all-time greatest hits. The Social Science Journal 40, 4 (2003), 643--650.
[10]
William L Hamilton, Jure Leskovec, and Dan Jurafsky. 2016. Cultural shift or linguistic drift? comparing two computational measures of semantic change. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Conference on Empirical Methods in Natural Language Processing, Vol. 2016. NIH Public Access, 2116.
[11]
William L Hamilton, Jure Leskovec, and Dan Jurafsky. 2016. Diachronic word embeddings reveal statistical laws of semantic change. arXiv preprint arXiv:1605.09096 (2016).
[12]
Liangjie Hong and Brian D Davison. 2010. Empirical study of topic modeling in twitter. In Proceedings of the first workshop on social media analytics. ACM, 80--88.
[13]
B. Logan, A. Kositsky, and P. Moreno. 2004. Semantic analysis of song lyrics. In 2004 IEEE International Conference on Multimedia and Expo (ICME) (IEEE Cat. No.04TH8763), Vol. 2. 827--830 Vol.2.
[14]
Laurens van der Maaten and Geoffrey Hinton. 2008. Visualizing data using t-SNE. Journal of machine learning research 9, Nov (2008), 2579--2605.
[15]
Matthias Mauch, Robert M MacCallum, Mark Levy, and Armand M Leroi. 2015. The evolution of popular music: USA 1960--2010. Royal Society open science 2, 5 (2015), 150081.
[16]
Rishabh Mehrotra, Scott Sanner, Wray Buntine, and Lexing Xie. 2013. Improving lda topic models for microblogs via tweet pooling and automatic labeling. In Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval. ACM, 889--892.
[17]
Rada Mihalcea and Carlo Strapparava. 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, 590--599.
[18]
Tomas Mikolov, Kai Chen, Greg Corrado, and Jeffrey Dean. 2013. Efficient Estimation of Word Representations in Vector Space. CoRR abs/1301.3781 (2013). arXiv:1301.3781 http://arxiv.org/abs/1301.3781
[19]
Robert Neumayer and Andreas Rauber. 2007. Integration of Text and Audio Features for Genre Classification in Music Information Retrieval. In Advances in Information Retrieval, Giambattista Amati, Claudio Carpineto, and Giovanni Romano (Eds.). Springer Berlin Heidelberg, Berlin, Heidelberg, 724--727.
[20]
André JM Prévos. 1996. The evolution of French rap music and hip hop culture in the 1980s and 1990s. French Review (1996), 713--725.
[21]
Maja Rudolph and David Blei. 2018. Dynamic Embeddings for Language Evolution. In Proceedings of the 2018 World Wide Web Conference (WWW '18). International World Wide Web Conferences Steering Committee, Republic and Canton of Geneva, Switzerland, 1003--1011.
[22]
Shoto Sasaki, Kazuyoshi Yoshii, Tomoyasu Nakano, Masataka Goto, and Shigeo Morishima. 2014. LyricsRadar: A Lyrics Retrieval System Based on Latent Topics of Lyrics. In Ismir. 585--590.
[23]
Shoto Sasaki, Kazuyoshi Yoshii, Tomoyasu Nakano, Masataka Goto, and Shigeo Morishima. 2014. LyricsRadar: A Lyrics Retrieval System Based on Latent Topics of Lyrics. In Ismir. 585--590.
[24]
Peter H Schönemann. 1966. A generalized solution of the orthogonal procrustes problem. Psychometrika 31, 1 (1966), 1--10.
[25]
Joan Serrà, Emilia Gómez, and Perfecto Herrera. 2010. Audio Cover Song Identification and Similarity: Background, Approaches, Evaluation, and Beyond. Springer Berlin Heidelberg, Berlin, Heidelberg, 307--332.
[26]
C. E. Shannon. 1948. A mathematical theory of communication. The Bell System Technical Journal 27, 3 (1948), 379--423.
[27]
Jieying She and Lei Chen. 2014. Tomoha: Topic model-based hashtag recommendation on twitter. In Proceedings of the 23rd International Conference on World Wide Web. ACM, 371--372.
[28]
Lauren Squires. 2019. Genre and linguistic expectation shift: Evidence from pop song lyrics. Language in Society 48, 1 (2019), 1--30.
[29]
Xuri Tang. 2018. A state-of-the-art of semantic change computation. Natural Language Engineering 24, 5 (2018), 649--676.
[30]
Kosetsu Tsukuda, Keisuke Ishida, and Masataka Goto. 2017. Lyric Jumper: A Lyrics-Based Music Exploratory Web Service by Modeling Lyrics Generative Process. In ISMIR. 544--551.
[31]
Zijun Yao, Yifan Sun, Weicong Ding, Nikhil Rao, and Hui Xiong. 2018. Dynamic Word Embeddings for Evolving Semantic Discovery. In Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining (WSDM '18). ACM, 673--681.
[32]
Feng Zhao, Yajun Zhu, Hai Jin, and Laurence T. Yang. 2016. A personalized hashtag recommendation approach using LDA-based topic model in microblog environment. Future Generation Computer Systems 65 (2016), 196 -- 206. http://www.sciencedirect.com/science/article/pii/S0167739X15003258 Special Issue on Big Data in the Cloud.
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Published In
October 2019
537 pages
ISBN:9781450367639
DOI:10.1145/3323503
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Published: 29 October 2019
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WebMedia '19
WebMedia '19: Brazilian Symposium on Multimedia and the Web
October 29 - November 1, 2019
Rio de Janeiro, Brazil
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