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Nov 6, 2015

Rap Analysis MIDS Project in VICE’s Motherboard

From Motherboard, from Vice Magazine

Rap Tracks Written by Algorithms May End Up Not Being Anything to F— With

by Ben Richmond

Given that they're the only millionaires who seem to understand how fun it is to be a millionaire, being a successful rapper seems like pretty much the best get-rich-quick-[or-die-trying] scheme imaginable. But artistic success can be elusive, which is why most rising hip-hop stars are forced to spend so much time creating high-profile Twitter beef instead of honing their craft. Fortunately, help is on the way.

In a fusion of something cool with something very square, Big Data has entered the realm of Biggie Smalls. Anthony Abraham and Nikhita Koul are two delightfully earnest hip-hop fans who recently graduated of the Master of Information and Data Science at UC Berkeley. For their capstone project, they and classmate Joe Morales designed the Rap Analysis Project (note: spells R.A.P.), which applies “machine learning techniques and data science principles to a database of rap lyrics from 1980 to 2015.” Among other things, they've produced a model that can predict, based on lyrics, whether a rap track will be a hit.

While their classmates were working on things like predicting baseball pitches and analyzing where electric cars are most likely to be purchased, the R.A.P. team was feeding thousands of rap lyrics into a database, trying to figure out what makes one song this year's “Trap Queen” and another stuck on a CD-R in some dude's pocket....

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R.A.P. - Rap Analysis Project was a MIDS Capstone Project by students Tony Abraham, Nikhita Koul, and Joseph Morales.

Last updated:

October 4, 2016