| Popis: |
K-pop, or Korean pop music, is a genre originating from South Korea that features various musical styles such as hip hop, R&B, and electronic dance. Modern K-pop started with Seo Taiji and Boys in 1992 and has since evolved through stylistic eras called 'generations' to become a worldwide sensation. K-pop's global popularity can be recognized by the success of groups such as BTS and BlackPink. How do the musical qualities of K-pop songs contribute to the genre's popularity? Furthermore, how have the musical qualities contributed to the evolution of becoming the global phenomenon it is today? To explore these questions and more, multivariate analysis will be performed on a curated dataset of 12,012 K-pop songs and their audio features. The audio features, collected with Spotify's Web API, include variables such as Danceability, Loudness, Acousticness, and Valence. The audio features contribution and trends in the evolution of K-pop will be analyzed with nonparametric statistical approaches, Multiple Linear Regression (MLR) and Logistic Regression models. MLR and Logistic Regression will also be used to examine the relationship between the audio features and popularity. Finally, dimension reduction of the audio features performed by Principal Components Analysis paired with K-means clustering will be utilized to explore the possibility of optimizing song clusters within K-pop. |
