Till Lindemann is a poet better known as the song writer and lead singer of Rammstein. It could be interesting to compare the texts band’s lyrics and his poems, maybe gaining a better insight in how both of them are made. For automated analysis, however, both kinds of texts might seem of little interest. With very short texts, the basis for statistical reasoning is too small. Indeed, we cannot reasonably apply the various readability indexes we usually employ when analyzing corpora. But if we restrict our statistical glimpse to some very elementary calculations, something similar to an “author’s footprint” might emerge. I will first use the TTR, the ratio between the number of single words (“types”) that appear in the text and the total number of words (“tokens”).Then I will have a look at the numeric relation between functional words (prepositions articles, conjunctions, etc.) and content words. Some characteristics of Till...
The literary quality of these texts is evident. If you have studied German literature, you cannot ignore the allusions not only to GDR songs and brothers Grimm, but also to Trakl and French Symbolism. But how can we analyze Rammstein texts? I want to make them read by the machine. The programming languages R and Python offer a lot of packages with interesting methods of getting into the texts. I am curious about how we can, by machine Learning, find something out.