Computational lexical analysis of Flamenco genres

TL;DR

This paper employs natural language processing and machine learning to analyze Flamenco lyrics, categorizing over 2000 songs into genres, revealing characteristic lexical patterns, semantic fields, and historical relationships among styles.

Contribution

It introduces a novel computational approach to classify Flamenco genres and analyze their semantic and historical relationships using NLP and network analysis.

Findings

Accurate genre classification using lexical features

Identification of semantic fields characterizing each style

Revelation of historical connections between Flamenco styles

Abstract

Flamenco, recognized by UNESCO as part of the Intangible Cultural Heritage of Humanity, is a profound expression of cultural identity rooted in Andalusia, Spain. However, there is a lack of quantitative studies that help identify characteristic patterns in this long-lived music tradition. In this work, we present a computational analysis of Flamenco lyrics, employing natural language processing and machine learning to categorize over 2000 lyrics into their respective Flamenco genres, termed as $\textit{palos}$. Using a Multinomial Naive Bayes classifier, we find that lexical variation across styles enables to accurately identify distinct $\textit{palos}$. More importantly, from an automatic method of word usage, we obtain the semantic fields that characterize each style. Further, applying a metric that quantifies the inter-genre distance we perform a network analysis that sheds light on the relationship between Flamenco styles. Remarkably, our results suggest historical connections and $\textit{palo}$ evolutions. Overall, our work illuminates the intricate relationships and cultural significance embedded within Flamenco lyrics, complementing previous qualitative discussions with quantitative analyses and sparking new discussions on the origin and development of traditional music genres.