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Cluster Analysis of Musical Attributes for Top Trending Songs

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dc.contributor.author Al-Beitawi , Zayd
dc.contributor.author Salehan, Mohammad
dc.contributor.author Zhang, Sonya
dc.date.accessioned 2020-01-04T07:09:37Z
dc.date.available 2020-01-04T07:09:37Z
dc.date.issued 2020-01-07
dc.identifier.isbn 978-0-9981331-3-3
dc.identifier.uri http://hdl.handle.net/10125/63756
dc.description.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.
dc.format.extent 7 pages
dc.language.iso eng
dc.relation.ispartof Proceedings of the 53rd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject AI and Cognitive Assistants in Collaboration
dc.subject clustering
dc.subject music streaming
dc.subject recommender systems
dc.title Cluster Analysis of Musical Attributes for Top Trending Songs
dc.type Conference Paper
dc.type.dcmi Text
dc.identifier.doi 10.24251/HICSS.2020.017
Appears in Collections: AI and Cognitive Assistants in Collaboration


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