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.
Highlights
Today, the music industry has continued to grow with the development of electronic devices and technology
As we described in the previous section, music contains 9 factors: bpm (BPM), nrgy, dnce, loudness, live, val, dur, acoustics, and speech
With the emergence of smart phone and streaming services, ranking high in streaming services has become an important factor for better profits and popularity
Summary
The music industry has continued to grow with the development of electronic devices and technology. Music concerts and sale rates of music CDs are the most revenue These days with the emergence of smartphone and music applications, music streaming, which is a way of delivering music without requiring users to download files from the internet. The artist can earn tons of money if the music ranks high on Spotify. The paper is going to implement the machine learning model that can predict the popularity or rank of music in Spotify. The model does not consider the name of the artist when it predicts the popularity of an unpopular artist can decide whether their music has the potential to be ranked in Spotify. The research expects that this model can be used by many artists or music companies to predict their rank before they release the music.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Science, Technology and Society
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
