Decoding the Style and Bias of Song Lyrics
This work addresses the lack of large-scale computational analysis of song lyrics, which is important due to music's widespread consumption and emotional impact, though it is incremental as it applies existing methods to new data.
The paper tackled the problem of computationally analyzing style and biases in song lyrics by analyzing over half a million songs across five decades, finding that popular songs differ significantly in style and that biases in lyrics correlate with societal biases.
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.