Data Science Approach to Compare the Lyrics of Popular Music Artists - Rosebaugh, Caleb; Shamir, Lior (Universitas Islam Indonesia, 2022)
Popular music lyrics exhibit clear differences between songwriters. This study describes a quantitative approach to the analysis of popular music lyrics. The method uses explainable measurements of the lyrics and therefore allows the use of quantitative measurements for consequent qualitative analyses. This study applies the automatic quantitative text analytics to 18,577 songs from 89 popular music artists. The analysis quantifies different elements of the lyrics that might be impractical to measure manually. The analysis includes basic supervised machine learning, and the explainable nature of the measurements also allows to identify specific differences between the artists. For instance, the sentiments expressed in the lyrics, the diversity in the selection of words, the frequency of gender-related words, and the distribution of the sounds of the words show differences between popular music artists. The analysis also shows a correlation between the easiness of readability and the positivity of the sentiments expressed in the lyrics. The analysis can be used as a new approach to studying popular music lyrics. The software developed for the study is publicly available and can be used for future studies of popular music lyrics.