  The central idea of this paper is to gain a deeper understanding of song
lyrics computationally. We focus on two aspects: style and biases of song
lyrics. All prior works to understand these two aspects are limited to manual
analysis of a small corpus of song lyrics. In contrast, we analyzed more than
half a million songs spread over five decades. We characterize the lyrics style
in terms of vocabulary, length, repetitiveness, speed, and readability. We have
observed that the style of popular songs significantly differs from other
songs. We have used distributed representation methods and WEAT test to measure
various gender and racial biases in the song lyrics. We have observed that
biases in song lyrics correlate with prior results on human subjects. This
correlation indicates that song lyrics reflect the biases that exist in
society. Increasing consumption of music and the effect of lyrics on human
emotions makes this analysis important.
