#SPOTIFY CHARTS 2017 SONGS FREE#

The audio features collected from Spotify cover a wide range of metrics, with some related to the content of the lyrics, some measuring objective properties of the song, and most interestingly, some measuring subjective properties like how danceable or positive-feeling a song is. The final de-duplicated dataset consists of 5566 singles, of which 5396 have Spotify audio feature data and 5516 have lyrics. Approximately 3% of all songs were not queried successfully from Spotify (though this is down from 10% when using the original dataset). Spotify's audio feature data was then accessed directly by querying Spotify's API using the Billboard Hot 100 songs obtained from Wikipedia. The vast majority of lyrics were obtained from ( 93.8%), with accounting for almost all of the remaining lyrics ( 5.6%). While scraping for lyrics failed in 3.6% of songs in the original dataset my group used, doing so with cleaned Wikipedia data resulted in failed scrapes accounting for less than 1% ( 0.78%) of all songs. changing from Hall & Oates to Hall and Oates). Diddy to Puff Daddy) and preparing them into a format suitable for conversion to a URL string (e.g. Much attention was paid to normalizing the names of artists and songs from the initial Wikipedia scrape (e.g. For each song, scrapes are conducted sequentially through the four websites if the scrape of a website fails, then the next website is attempted unless all four have already been exhausted. Fortunately,, ,, and serve lyrics on webpages that follow consistent URL naming conventions, meaning the lyric data could also be scraped. Many music services offer API's to query for song lyrics, but all either charge a fee, limit the number of songs that can be queried, or return only a proportion of the lyrics. There were 235 such duplicates, not counting the original songs, and they were removed from the dataset.Ĭollecting lyrics for each song proved more involved.

Because some songs are released at the end of the year, they might chart in the year of its release and in the following year. To obtain songs and rankings from Billboard's Year-End Hot 100 charts, I scraped the data from relevant Wikipedia tables for 1960- 2017. Such an issue posed many obstacles to a clean analysis of the lyrics. A significant proportion of lyrics in the original dataset contained fused words, where the last word of a line was combined with the first word of the next line.As of writing, the Billboard Year-End charts that are available extend from 1960 to 2017, providing 8 more years of data from which to extrapolate trends.However, I wanted to build my own dataset for two reasons: The dataset we originally used consisted of Billboard Year-End Hot 100 Singles from 1965 to 2015 and each single's corresponding lyrics. The combination of data from the Billboard rankings, song lyrics, and Spotify audio features provides many avenues for rich and interesting analysis.

To build on these ideas, for each song, we also incorporated audio-feature data (such as loudness, tempo, danceability, etc.) maintained by Spotify. Many text mining analyses have been performed on the lyrics of these songs, with Kaylin Pavlik's, whose dataset my group used in the original project, among the most notable. Billboard Magazine publishes a yearly chart, the Year-End Hot 100 that ranks the best-performing singles of the United States based on data collected from physical sales, digital sales, radio airplay, and streaming. Popular music offers a unique lens through which to study what preoccupies a society, what people value in their entertainment, and how cultural preferences change with each generation.

#SPOTIFY CHARTS 2017 SONGS FREE#

After finishing the class and free to explore the data without constraints, I decided to revisit and extend the project in Python for a more comprehensive analysis. However, because the project was conducted primarily in R despite our much greater proficiency with Python, we were relatively hamstrung and had to limit the scope and depth of our explorations. In that project, we examined the lyrics and audio features of America's most popular songs from 1965- 2015 to gain insights about how our culture and music industry has evolved. in Data Science program called STAT 5702: Exploratory Data Analysis and Visualization. This analysis originated as a group-based final project for a course in my M.S. Better Technology and Worse Attention Spans.Exploring, Analyzing, and Modeling 58 years of Popular Music

Spotify charts 2017 songs