Billboard has published a Year-End Hot 100 every December since 1958. The chart measures the performance of singles in the U.S. throughout the year. Using R, I’ve combined the lyrics from 50 years of Billboard Year-End Hot 100 (1965-2015) into one dataset for analysis. You can download that dataset here.
The songs used for analysis were scraped from Wikipedia’s entry for each Billboard Year-End Hot 100 Songs (e.g., 2014). This is the year-end chart, not weekly rankings. Many artists have made the weekly chart but not the final year end chart. The final chart is calculated using an inverse point system based on the weekly Billboard charts (100 points for a week at number one, 1 point for a week at number 100, etc).
I used the xml and RCurl packages to scrape song and artist names from each Wikipedia entry. I then used that list to scrape lyrics from sites that had predictable URL strings (for example, metrolyrics.com uses metrolyrics.com/SONG-NAME-lyrics-ARTIST-NAME.html). If the first site scrape failed, I moved onto the second, and so on. About 78.9% of the lyrics were scraped from metrolyics.com, 15.7% from songlyrics.com, 1.8% from lyricsmode.com. About 3.6% (187/5100) were unavailable.
The dataset features 5100 observations with the features rank (1-100), song, artist, year, lyrics, and source. The artist feature is fairly standardized thanks to Wikipedia, but there is still quite a bit of noise when it comes to artist collaborations (Justin Timberlake featuring Timbaland, for example). If there were any errors in the lyrics that were scraped, such as spelling errors or derivatives like "nite" instead of "night," they haven't been corrected.
Full analysis can be found here.