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.

翻译：弗拉门戈被联合国教科文组织列为人类非物质文化遗产，是根植于西班牙安达卢西亚的文化认同的深刻表达。然而，目前仍缺乏量化研究来识别这一悠久音乐传统中的特征模式。本研究对弗拉门戈歌词进行了计算分析，运用自然语言处理与机器学习技术，将2000余首歌词归类至其对应的弗拉门戈流派（即$\textit{palos}$）。通过使用多项式朴素贝叶斯分类器，我们发现不同风格间的词汇变异能够准确区分不同的$\textit{palos}$。更重要的是，我们采用一种自动化的词语用法分析方法，获得了每种风格的特征语义场。进一步应用量化流派间距离的度量指标，我们进行了网络分析，揭示了弗拉门戈流派之间的关系。值得注意的是，我们的研究结果暗示了历史关联与$\textit{palo}$的演变过程。总体而言，本研究揭示了弗拉门戈歌词中蕴含的复杂关系与文化意义，以量化分析补充了先前的定性讨论，并为传统音乐流派的起源与发展提供了新的探讨方向。