Humble Mumble: Text Mining Outkast

An OutKast Reader: Essays on Race, Gender, and the Postmodern South

October 1, 2021

Published by University of Georgia Press

By Howard Rambsy II & Kenton Rambsy

The growth of Hip Hop Studies over the last two decades has prompted new and exciting developments in African American literary studies and Black Studies. College professors regularly offer courses on rap music. In addition, scholarly and journalistic writings on hip hop continue to appear with regularity. The abundance of publishing and pedagogical activities associated with Hip Hop Studies indicates the vibrancy of the field. Nonetheless, thorough investigations of rap music utilizing digital tools such as data mining software await further exploration.

This essay addresses the imperative of applying digital tools to the study of rap music by concentrating on OutKast, an outstanding rap group who remains the focal point of considerable fanfare while comparatively understudied in scholarly contexts. We make the case that data mining elucidates the quantity and complexity of OutKast lyrics and paves the way for new close and distant readings their creative output. To support our contention, we present findings from a dataset derived from text mining Andre 3000 and Big Boi’s first five albums. In addition, we highlight their lyrical divergences, which simultaneously accounts for the tensions and originality of their works. Our results validate the utility of utilizing text mining software to analyze OutKast lyrics, and more broadly, our work confirms the importance of situating Hip Hop Studies in the context of digital humanities.