  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.
