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Cluster Analysis of Musical Attributes for Top Trending Songs

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Title:Cluster Analysis of Musical Attributes for Top Trending Songs
Authors:Al-Beitawi , Zayd
Salehan, Mohammad
Zhang, Sonya
Keywords:AI and Cognitive Assistants in Collaboration
clustering
music streaming
recommender systems
Date Issued:07 Jan 2020
Abstract:Music streaming services like Spotify have changed the way consumers listen to music. Understanding what attributes make certain songs trendy can help services to create a better customer experience as well as more effective marketing efforts. We performed cluster analysis on Top 100 Trending Spotify Song of 2017, with ten attributes, including danceability, energy, loudness, speechiness, acousticness, instrumentalness, Liveness, valence, tempo, and duration. The results show that music structures with high danceability and low instrumentalness increase the popularity of a song and lead them to chart-topping success.
Pages/Duration:7 pages
URI:http://hdl.handle.net/10125/63756
ISBN:978-0-9981331-3-3
DOI:10.24251/HICSS.2020.017
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: AI and Cognitive Assistants in Collaboration


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