Computational Lexical Analysis of Flamenco Genre: Natural Language Processing and Machine Learning Approaches

Table of Contents

• 1. Gabatarwa
• 2. Hanyar
  • 2.1 Tattara Bayanai
  • 2.2 Shiri Rubutu
  • 2.3 Fitattun Siffofi
• 3. Rarrabawar Injin Koyo
  • 3.1 Multinomial Naive Bayes
  • 3.2 Model Evaluation
• 4. Semantic Field Analysis
• 5. Binciken hanyoyin sadarwa na alaƙar mazhabu
• 6. Sakamako da Tattaunawa
• 7. Technical Framework and Mathematical Foundation
• 8. Analytical Framework Example
• 9. Hangon Aikace-aikace da Bincike na Gaba
• 10. Bincike Mai Zurfi: Fahimta ta Asali da Kimantawa
• 11. Littattafan da aka yi amfani da su

1. Gabatarwa

Flamenco, recognized by UNESCO as Intangible Cultural Heritage, is a profound expression of cultural identity in Spain's Andalusia region. This study computationally analyzes over 2,000 lyrics spanning different flamenco styles (palos), addressing a gap in quantitative research in this field. The research demonstrates how lexical variation enables accurate genre classification and reveals distinctive semantic patterns characteristic of each style.

2. Hanyar

2.1 Tattara Bayanai

The study constructed a comprehensive corpus containing 2,147 flamenco lyrics, spanning genres such as Soleá, Bulerías, Seguiriyas, and Tangos. Data were sourced from professional flamenco archives and validated by domain experts to ensure authenticity.

2.2 Shiri Rubutu

Text normalization includes lowercase conversion, stop word removal, and stemming based on Spanish linguistic rules. Special emphasis is placed on preserving Flamenco-specific terminology and fixed expressions.

2.3 Fitattun Siffofi

Yi lissafta TF-IDF vector na kowane daftarin aiki, ta amfani da n-gram range (1,2) don kama kalmomi guda ɗaya da kuma sauran jimloli na yau da kullun.

3. Rarrabawar Injin Koyo

3.1 Multinomial Naive Bayes

Classification employs the Multinomial Naive Bayes algorithm, with its probability calculation formula: $P(c|d) \propto P(c) \prod_{i=1}^{n} P(w_i|c)^{x_i}$, where $P(c|d)$ denotes the probability of class $c$ given document $d$, $P(c)$ is the prior probability of class $c$, and $P(w_i|c)$ represents the probability of word $w_i$ given class $c$.

3.2 Model Evaluation

The model achieved 84.3% accuracy in cross-validation, with precision and recall rates for major genres both exceeding 80%. Confusion matrix analysis revealed the highest confusion between historically related genres.

4. Semantic Field Analysis

By automatically identifying the characteristic semantic fields of each genre, unique thematic patterns are revealed. Soleá lyrics emphasize suffering and religious themes, while Bulerías more often showcase festive and social content. This analysis employs a cross-genre relative frequency comparison method.

5. Binciken hanyoyin sadarwa na alaƙar mazhabu

Yin amfani da Jensen-Shannon divergence don auna tazarar tsakanin makarantu: $D_{JS}(P||Q) = \frac{1}{2}D_{KL}(P||M) + \frac{1}{2}D_{KL}(Q||M)$, inda $M = \frac{1}{2}(P+Q)$. Tsarin clustering da aka nuna a cikin cibiyar sadarwa na gani ya yi daidai da tarihin ci gaban Flamenco.

6. Sakamako da Tattaunawa

Binciken ya tabbatar da cewa tsarin kalmomi na iya zama alama mai aminci don rarrabe makarantun Flamenco. Binciken cibiyar sadarwa ya ba da shaidar ƙididdiga na alaƙar tarihi tsakanin makarantu, yana ba da goyan baya na lissafi ga ka'idar kiɗan gargajiya.

7. Technical Framework and Mathematical Foundation

Binciken ya yi amfani da cikakkiyar tsarin sarrafa harshe na halitta, gami da raba kalmomi, zaɓin siffa dangane da ƙididdiga na chi-square $\chi^2(t,c) = \sum_{e_t\in\{0,1\}}\sum_{e_c\in\{0,1\}} \frac{(N_{e_te_c} - E_{e_te_c})^2}{E_{e_te_c}}$, da rage girman batu ta hanyar binciken tushen batu. Ƙa'idar lissafi ta yi daidai da hanyoyin ilimin harshe na lissafi a cikin binciken sarrafa harshe na halitta.

8. Analytical Framework Example

Nazarin Shari'ar: Binciken Salon Soleá
Shigarwa: Rubutun Waƙoƙin Asali → Gabatarwa (Cire Kalmomin Tsayawa, Ciro Tushen Kalmomi) → Cizon Siffofi (Vekin TF-IDF) → Rarrabuwa (Multinomial Naive Bayes) → Gano Filin Ma'ana → Fitowa: Ƙwarin Rarrabuwar Nau'i 0.92, Gano Abubuwan Jigo Maɓalli: 'Zuciya' (Mita: 0.045), 'Raɗaɗɗen Zuciya' (0.038), 'Allah' (0.031).

9. Hangon Aikace-aikace da Bincike na Gaba

Potential applications include automated organization of flamenco archives, educational tools for flamenco studies, and cross-cultural music analysis. Future research should integrate audio features by leveraging models from the Music Information Retrieval field, extend to other oral traditions, and develop real-time classification systems suitable for live performances.

10. Bincike Mai Zurfi: Fahimta ta Asali da Kimantawa

Fahimta Ta Asali:Wannan bincike ya yi nasarar cike gibin da ke tsakanin ilimin kiɗa na al'ada da nazarin lissafi, yana tabbatar da cewa al'adar baka ta Flamenko ta ƙunshi ƙirar kalmomi masu iya aunawa waɗanda ke nuna bambance-bambancen salon fasaha daidai. Ya nuna cewa, maganganun al'adu waɗanda a da ake ganin ba za a iya ƙidaya su saboda suna da na zahiri, a zahiri ana iya yin nazari mai tsari a kansu.

Tsarin Ma'ana:The research follows a meticulously designed process, progressing logically from data collection through preprocessing, feature extraction, classification, and finally network analysis. Each phase builds upon the preceding steps, constructing a comprehensive analytical framework. The transition from individual genre classification to mapping inter-genre relationships reflects a sophisticated research design.

Strengths and Limitations:The study's primary strength lies in innovatively applying established natural language processing methods to a previously underexplored domain. The utilization of multiple analytical approaches (classification, semantic analysis, network theory) provides triangulation. However, potential sampling bias in lyric selection exists, and musical features crucial to flamenco expression remain unaddressed. The absence of temporal dimension analysis limits insights into genre evolution.

Shawawarwar shawaraCibiyoyin al'adu yakamata su yi amfani da irin wannan hanyar lissafi don lissafita al'adun baka. Masu bincike suna buƙatar ƙetare binciken kalmomi, su faɗaɗa zuwa hanyoyin da suka haɗa da siffofi na sauti. Wannan hanyar tana nuna yuwuwar amfani da sauran al'adun baka, tun daga yaren ganga na Afirka zuwa al'adun labarun Native American. Ayyukan gaba yakamata su kwaikwayi hanyoyin ilimin harshe na tarihi, suna gabatar da siginar lokaci don bin diddigin ci gaban salon.

11. Littattafan da aka yi amfani da su

1. UNESCO. (2010). Flamenco an ayyana a matsayin Gadojin Al'adar Baki ta Bil'adama.
2. Manning, C.D., et al. (2014). Foundations of Statistical Natural Language Processing.
3. McCallum, A., Nigam, K. (1998). A Comparison of Event Models for Naive Bayes Text Classification.
4. Knight, S. (2018). Computational Approaches to Ethnomusicology.
5. Müller, M. (2015). Fundamentals of Music Processing.
6. Goodfellow, I., et al. (2016). Deep Learning (for technical methodology comparison).