Keyness in song lyrics: Challenges of highly clumpy data
Research output: Contribution to journal › Research article › Contributed › peer-review
Computer-assisted stylistic analyses regularly employ the calculation of keywords. We show that the inclusion of a separate dispersion measure in addition to a frequency measure into keyword analysis (or more generally: keyness analysis), as proposed by Gries (2021), is a necessary extension of said analyses. Using texts from the German Songkorpus, we demonstrate that traditional keyword calculations using only frequency measures lead to spurious results. Determining keywords by both measuring a word’s frequency and its dispersion in comparison to a reference corpus gives a more realistic view. This is especially relevant for our corpus, since song lyrics turn out to be extraordinarily clumpy data: Words that are very frequent in one artist’s subcorpus typically only occur in a few or even just a single one of their songs due to widespread word repetition within songs, e.g., in choruses. Song lyrics in our dataset are shown to not feature words that can be considered key at all. Our contribution is twofold: (1) We demonstrate the utility of Gries’ (2021) approach and (2) interpret the (lack of) results in terms of a genre-specific property which is that song lyrics are lexically autonomous works of art.
|Number of pages||18|
|Journal||Journal for Language Technology and Computational Linguistics|
|Publication status||Published - 1 May 2023|