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A Bayesian Approach to Understanding Music Popularity

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Date
2015-05-08
Author
Shapiro, Heather
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Abstract
The Billboard Hot 100 has been the main record chart for popular music in the American music industry since its first official release in 1958. Today, this rank- ing is based upon the frequency of which a song is played on the radio, streamed online, and its sales. Over time, however, the limitations of the chart have become more pronounced and record labels have tried different strategies to maximize a song’s potential on the chart in order to increase sales and success. This paper intends to analyze metadata and audio analysis features from a random sample of one million popular tracks, dating back to 1922, and assess their potential on the Billboard Hot 100 list. We compare the results of Bayesian Additive Regression Trees (BART) to other decision tree methods for predictive accuracy. Through the use of such trees, we can determine the interaction and importance of differ- ent variables over time and their effects on a single’s success on the Billboard chart. With such knowledge, we can assess and identify past music trends, and provide producers with the steps to create the ‘perfect’ commercially successful song, ultimately removing the creative artistry from music making.
Type
Honors thesis
Department
Statistical Science
Subject
Bayesian
BART
popular music
regression trees
sentiment analysis
American music industry
Permalink
https://hdl.handle.net/10161/9747
Citation
Shapiro, Heather (2015). A Bayesian Approach to Understanding Music Popularity. Honors thesis, Duke University. Retrieved from https://hdl.handle.net/10161/9747.
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This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 United States License.

Rights for Collection: Undergraduate Honors Theses and Student papers


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