Predicting song popularity based on Spotify's audio features : insights from the Indonesian streaming users

Gespeichert in:

Autor*in:	Saragih, Harriman Samuel [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	audio features consumer culture machine learning popularity Spotify

Übergeordnetes Werk:	Enthalten in: Journal of management analytics - Abingdon, Oxon [u.a.] : Taylor & Francis, 2014, 10(2023), 4, Seite 693-709
Übergeordnetes Werk:	volume:10 ; year:2023 ; number:4 ; pages:693-709

Links:	Link aufrufen Link aufrufen

DOI / URN:	10.1080/23270012.2023.2239824

Katalog-ID:	1877641766

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author	Saragih, Harriman Samuel
spellingShingle	Saragih, Harriman Samuel misc audio features misc consumer culture misc machine learning misc popularity misc Spotify Predicting song popularity based on Spotify's audio features insights from the Indonesian streaming users
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topic_title	26 00 DE-206 Using regression and classification machine learning algorithms, this study explores audio features on Spotify that contribute to the popularity of songs streamed in Indonesia, and then evaluates the feature importance for prediction. The publicly accessible Kaggle data consists of 92,755 rows and 20 columns. Using multiple model comparisons for regression and classification, this study identifies Extra Trees Regressor and Random Forest Classifier as the two predictive approaches with the highest accuracy. This study contributes to the scientific literature on hit songs by examining the influence of audio features on a song's popularity using both classification and regression machine learning methods, with an emphasis on Indonesia based on consumer culture theory Predicting song popularity based on Spotify's audio features insights from the Indonesian streaming users Harriman Samuel Saragih audio features (dpeaa)DE-206 consumer culture (dpeaa)DE-206 machine learning (dpeaa)DE-206 popularity (dpeaa)DE-206 Spotify (dpeaa)DE-206
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title_short	Predicting song popularity based on Spotify's audio features
url	https://www.tandfonline.com/doi/pdf/10.1080/23270012.2023.2239824 https://doi.org/10.1080/23270012.2023.2239824
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