Music Popularity Prediction Through Data analysis of Music’s Characteristics

Jaehyun Kim

doi:doi:10.11648/j.ijsts.20210905.16

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Music Popularity Prediction Through Data analysis of Music’s Characteristics

Jaehyun Kim

Published in International Journal of Science, Technology and Society (Volume 9, Issue 5)

Received: 7 October 2021 Accepted: 20 October 2021 Published: 29 October 2021

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Abstract

Today, the music industry has grown tremendously with the emergence of smartphones and streaming services. In the past, the most of revenue was from the album’s sales and concerts. However, these days, streaming services on the web or smartphones have become a huge part of the music industry. Therefore, from an artist’s perspective, it is important to rank their music high on streaming services to earn money. While the music industry is growing, the top 1% of artists have gone from earning 26 percent of revenue to between 56% and 77%. This shows the huge income gap among the artists. Large profit on various artist can help to make a better music business. This paper is written in order to analyze the popular music in Spotify, which is one of the most popular music streaming services in the world. To find the factors that popular music has, this paper analyzes data of 2010~2019 top 50 music on Spotify. The paper also presents the table and graph that clearly illustrate the average of many music factors such as beat per minute and duration to investigate how music should be made to rank high on the Spotify. Moreover, the paper utilizes a machine learning model to predict the popularity of music by analyzing the beat per minute, speecheness, loudness, and duration, etc. The prediction model is expected to be used by many artists or music companies before they release their music.

Published in	International Journal of Science, Technology and Society (Volume 9, Issue 5)
DOI	10.11648/j.ijsts.20210905.16
Page(s)	239-244
Creative Commons	This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.
Copyright	Copyright © The Author(s), 2021. Published by Science Publishing Group

Keywords

Data Science, Machine Learning, EDA, Music, Business

References

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[2]	Álvarez, Ricardo. “The music industry in the dawn of the 21st century”. 2017. 10.13140/RG.2.2.32360.67847.
[3]	M. Inter, K. Ka, L. Wang, J. Yang, Z. Yu and N. Ko “Musical trends and predictability of success in contemporary songs in and out of the top charts”, May 16 2018.
[4]	A. Varshavsky, “Analysis of income inequality impact on the musical art”, Journal of the New Economic Association, New Economic Association, 2020.
[5]	P. Dicola, “Money from Music: Survey Evidence on Musicians’ Revenue and Lessons About Copyright Incentives, Northwestern university School of Law, 2019.
[6]	M. Mai, “Death of the Music Long Tail”, silpayamanant.2014, https://silpayamanant.wordpress.com/2014/03/07/death-of-the-musical-long-tail/.
[7]	“U.S. music streaming revenue 2020”, Statista, 2021. https://www.statista.com/statistics/437717/music-streaming-revenue-usa/
[8]	Seth A. Carver “Changing the Industry, Spotify”, University of Tennessee, 2016.
[9]	Fleicher, Rasmus & Snickars, Pelle. “Discovering Spotify - A Thematic Introduction. Culture Unbound”: Journal of Current Cultural Research. 9. 2017. 130-145. 10.3384/cu.2000.1525.1792130.
[10]	Araujo, Carlos & Cristo, Marco & Giusti, Rafael. “Predicting Music Popularity on Streaming Platforms”. 2019. 141-148. 10.5753/sbcm.2019.10436.
[11]	scikit-learn developers.. sklearn. neighbors. kneighborsregressor. scikit learn. https://scikit-learn.org/stable/modules/generated/sklearn.neighbors.KNeighborsRegressor.html.
[12]	Kumari, Khushbu & Yadav, Suniti. “Linear regression analysis study”. Journal of the Practice of Cardiovascular Sciences. 2018 4. 33. 10.4103/jpcs.jpcs_8_18.
[13]	Teixeira-Pinto, A. 2 “K-nearest Neighbours Regression \| Machine Learning for Biostatistic”. Biostatistics Statistics Collaboration of Australia. 2021. https://bookdown.org/tpinto_home/Regression-and-Classification/k-nearest-neighbours-regression.html
[14]	Ali, Jehad & Khan, Rehanullah & Ahmad, Nasir & Maqsood, Imran. “Random Forests and Decision Trees”. 2012. International Journal of Computer Science Issues (IJCSI). 9.
[15]	Gesrad. B “Analysis of a Random Forest Model, Journal of Machine Learning Research, 2012.

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Jaehyun Kim. (2021). Music Popularity Prediction Through Data analysis of Music’s Characteristics. International Journal of Science, Technology and Society, 9(5), 239-244. https://doi.org/10.11648/j.ijsts.20210905.16

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Jaehyun Kim. Music Popularity Prediction Through Data analysis of Music’s Characteristics. Int. J. Sci. Technol. Soc. 2021, 9(5), 239-244. doi: 10.11648/j.ijsts.20210905.16

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Jaehyun Kim. Music Popularity Prediction Through Data analysis of Music’s Characteristics. Int J Sci Technol Soc. 2021;9(5):239-244. doi: 10.11648/j.ijsts.20210905.16

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@article{10.11648/j.ijsts.20210905.16,
  author = {Jaehyun Kim},
  title = {Music Popularity Prediction Through Data analysis of Music’s Characteristics},
  journal = {International Journal of Science, Technology and Society},
  volume = {9},
  number = {5},
  pages = {239-244},
  doi = {10.11648/j.ijsts.20210905.16},
  url = {https://doi.org/10.11648/j.ijsts.20210905.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsts.20210905.16},
  abstract = {Today, the music industry has grown tremendously with the emergence of smartphones and streaming services. In the past, the most of revenue was from the album’s sales and concerts. However, these days, streaming services on the web or smartphones have become a huge part of the music industry. Therefore, from an artist’s perspective, it is important to rank their music high on streaming services to earn money. While the music industry is growing, the top 1% of artists have gone from earning 26 percent of revenue to between 56% and 77%. This shows the huge income gap among the artists. Large profit on various artist can help to make a better music business. This paper is written in order to analyze the popular music in Spotify, which is one of the most popular music streaming services in the world. To find the factors that popular music has, this paper analyzes data of 2010~2019 top 50 music on Spotify. The paper also presents the table and graph that clearly illustrate the average of many music factors such as beat per minute and duration to investigate how music should be made to rank high on the Spotify. Moreover, the paper utilizes a machine learning model to predict the popularity of music by analyzing the beat per minute, speecheness, loudness, and duration, etc. The prediction model is expected to be used by many artists or music companies before they release their music.},
 year = {2021}
}

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TY - JOUR
T1 - Music Popularity Prediction Through Data analysis of Music’s Characteristics
AU - Jaehyun Kim
Y1 - 2021/10/29
PY - 2021
N1 - https://doi.org/10.11648/j.ijsts.20210905.16
DO - 10.11648/j.ijsts.20210905.16
T2 - International Journal of Science, Technology and Society
JF - International Journal of Science, Technology and Society
JO - International Journal of Science, Technology and Society
SP - 239
EP - 244
PB - Science Publishing Group
SN - 2330-7420
UR - https://doi.org/10.11648/j.ijsts.20210905.16
AB - Today, the music industry has grown tremendously with the emergence of smartphones and streaming services. In the past, the most of revenue was from the album’s sales and concerts. However, these days, streaming services on the web or smartphones have become a huge part of the music industry. Therefore, from an artist’s perspective, it is important to rank their music high on streaming services to earn money. While the music industry is growing, the top 1% of artists have gone from earning 26 percent of revenue to between 56% and 77%. This shows the huge income gap among the artists. Large profit on various artist can help to make a better music business. This paper is written in order to analyze the popular music in Spotify, which is one of the most popular music streaming services in the world. To find the factors that popular music has, this paper analyzes data of 2010~2019 top 50 music on Spotify. The paper also presents the table and graph that clearly illustrate the average of many music factors such as beat per minute and duration to investigate how music should be made to rank high on the Spotify. Moreover, the paper utilizes a machine learning model to predict the popularity of music by analyzing the beat per minute, speecheness, loudness, and duration, etc. The prediction model is expected to be used by many artists or music companies before they release their music.
VL - 9
IS - 5
ER -

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Author Information

Jaehyun Kim

Cheongshim International Academy, Gyeonggi-do, Korea

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APA Style

Jaehyun Kim. (2021). Music Popularity Prediction Through Data analysis of Music’s Characteristics. International Journal of Science, Technology and Society, 9(5), 239-244. https://doi.org/10.11648/j.ijsts.20210905.16

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ACS Style

Jaehyun Kim. Music Popularity Prediction Through Data analysis of Music’s Characteristics. Int. J. Sci. Technol. Soc. 2021, 9(5), 239-244. doi: 10.11648/j.ijsts.20210905.16

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AMA Style

Jaehyun Kim. Music Popularity Prediction Through Data analysis of Music’s Characteristics. Int J Sci Technol Soc. 2021;9(5):239-244. doi: 10.11648/j.ijsts.20210905.16

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@article{10.11648/j.ijsts.20210905.16,
  author = {Jaehyun Kim},
  title = {Music Popularity Prediction Through Data analysis of Music’s Characteristics},
  journal = {International Journal of Science, Technology and Society},
  volume = {9},
  number = {5},
  pages = {239-244},
  doi = {10.11648/j.ijsts.20210905.16},
  url = {https://doi.org/10.11648/j.ijsts.20210905.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ijsts.20210905.16},
  abstract = {Today, the music industry has grown tremendously with the emergence of smartphones and streaming services. In the past, the most of revenue was from the album’s sales and concerts. However, these days, streaming services on the web or smartphones have become a huge part of the music industry. Therefore, from an artist’s perspective, it is important to rank their music high on streaming services to earn money. While the music industry is growing, the top 1% of artists have gone from earning 26 percent of revenue to between 56% and 77%. This shows the huge income gap among the artists. Large profit on various artist can help to make a better music business. This paper is written in order to analyze the popular music in Spotify, which is one of the most popular music streaming services in the world. To find the factors that popular music has, this paper analyzes data of 2010~2019 top 50 music on Spotify. The paper also presents the table and graph that clearly illustrate the average of many music factors such as beat per minute and duration to investigate how music should be made to rank high on the Spotify. Moreover, the paper utilizes a machine learning model to predict the popularity of music by analyzing the beat per minute, speecheness, loudness, and duration, etc. The prediction model is expected to be used by many artists or music companies before they release their music.},
 year = {2021}
}

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