Cluster Analysis of Musical Attributes for Top Trending Songs

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dc.contributor.authorAl-Beitawi , Zayd
dc.contributor.authorSalehan, Mohammad
dc.contributor.authorZhang, Sonya
dc.date.accessioned2020-01-04T07:09:37Z
dc.date.available2020-01-04T07:09:37Z
dc.date.issued2020-01-07
dc.description.abstractMusic 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.extent7 pages
dc.identifier.doi10.24251/HICSS.2020.017
dc.identifier.isbn978-0-9981331-3-3
dc.identifier.urihttp://hdl.handle.net/10125/63756
dc.language.isoeng
dc.relation.ispartofProceedings of the 53rd Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAI and Cognitive Assistants in Collaboration
dc.subjectclustering
dc.subjectmusic streaming
dc.subjectrecommender systems
dc.titleCluster Analysis of Musical Attributes for Top Trending Songs
dc.typeConference Paper
dc.type.dcmiText

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