Analysis and Clustering of Pakistani Music by Lyrics: A Study of CokeStudio Pakistan

Clicks: 2
ID: 312872
2024
Free PDF
Article Quality & Performance Metrics
Overall Quality
Not rated
Combines reader engagement with the AI quality analysis. This article has not been analysed, so there is no overall score — reader engagement is measured and shown alongside.
AI Quality Assessment
Not analyzed
Readership in this journal
Emerging

Ranked #435 of 705 articles by views in Journal of Computing & Biomedical Informatics

Most read Least read

Bar heights use a square-root scale. Only the 120 most-read articles are drawn; the journal has 705 in total.

Mint this article as an NFT
Not yet minted

Create a permanent, verifiable on-chain record of this article on the Scimatic Network. The NFT is held in your Journament account, and you can withdraw it to your own wallet at any time.

5 SUSD one-off · no wallet required
Sign in to mint
Abstract
This research explores the application of unsupervised learning techniques to categorize and understand the lyrical content of CokeStudio songs. In a world where music transcends cultural boundaries, this study delves into the rich linguistic tapestry of lyrics, unraveling emotions, themes, and cultural nuances. We begin by employing Natural Language Processing (NLP) and analysis techniques to uncover the emotional underpinnings of these lyrical compositions. This emotional layering becomes the foundation for the subsequent clustering process. Multiple unsupervised learning algorithms, including K-Means, Hierarchical Clustering, and DBSCAN, are employed to categorize songs into thematic clusters. The quality of these clusters is assessed using the silhouette score, with the optimal number of clusters determined as 5, achieving a score of 0.41641. Furthermore, we develop a robust classification model utilizing machine learning algorithms such as Logistic Regression, Decision Trees, Random Forest, Gradient Boosting, and Multinomial Naive Bayes for evaluation of our clustering. This model assigns CokeStudio songs to thematic clusters based on the results of topic modeling, enhancing our understanding of the cultural and emotional dimensions of these compositions. Logistic Regression, with SMOTE applied to NMF values, emerges as the best-performing model, achieving an impressive testing score of 89.47%. The research findings not only illuminate the intricate emotions and narratives woven into CokeStudio songs but also emphasize the practical application of machine learning in music analysis. By identifying and classifying thematic clusters within song lyrics, this study enriches our comprehension of cultural expressions through music and opens avenues for personalized music recommendations.
Reference Key
imported_1777057483_69ebbecbb6ed2 Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Sheeraz Ahmad
Journal Journal of Computing & Biomedical Informatics
Year 2024
DOI
DOI not found
URL
https://jcbi.org/index.php/Main/article/view/503
Keywords Keywords not found

Citations

No citations found. To add a citation, contact the admin at info@scimatic.org

Comments

No comments yet. Be the first to comment on this article.