Rammstein Read by the Machine

Enumerating words can be helpful. With a simple command (tokens_ngrams), in R we also got lists of word pairs and triples and so on. But usually we want to know which words appear within the same documents. W hat about co-occurences within the same song? Which words appear in the same songs? With the fcm() command in R we obtain a neat little table, more or less like the following .                      ich      du      gut      liebe      kalt      bitte      sonne ich              2775 1289  474    550      352      25       143 du             0       920  209   183       279      25       19 gut             0        0       91        19          0           2           20 lust           627    1     2         0           280     0          1 deutschl  209    532  0         76        152      0         171 liebe                                             140        55        41           11 kalt                                                                  84        28        

  As   we have seen , love ("liebe") is, once the stopwords are eliminated,  the most frequent feature in Rammstein texts. Among all the nouns indicating elementary states and matters, like "Wasser", "Sonne", "Licht" and "Lust", "Love" looks surprising. Usually, we do not consider love an elementary feeling. Rammstein, as far as it seems, do. But still, we have to be cautious, as we do not even know whether the feature"liebe" corresponds to a noun or to a verb.  With a case-sensitive context research we obtain "Liebe"   28 times  "liebe"   18 occurrences. In "was ich liebe" (2019): "ich liebe nicht, dass ich was liebe", in "OK" (2019): "ich liebe dich, wenn du mich lässt". In the same song, we hear "was sich liebt, das darf sich lecken". Various forms of the same verb, which are counted separately. We can check them, one by one, and get: 0 "li

Usually, text analyzing programs consider "ich" ("I", "me") a stopword, a functional word without special meaning.  This is due to the fact that grammars categorize "ich" as a pronoun. But, as Eugen Coseriu stated in his book "Introduction to the linguistics of texts" (German edition 1985), this does not correspond to the real use of this word.  While "he" in  "Ralf is tired. He will go to bed soon"  is a substitute, i.e. pro-noun, for "Ralf", "I" in  "Ralf is tired. I am going to bed"  is not.  The "I " here is understood as reference to a second person. T he same is true of  "du "  ( "you " ).  "Ich " and  "Du ",  these two words have a deictic function, they indicate somebody. Hence, we should not treat them as functional words or eliminate them as stopwords. Especially in literary texts, which have, according not only to Habermas (198

  The practical part of introductions to Text Analysis with R usually starts to the sound of the hunting horn. "Tokenization" is the parole, and "elimination" the first goal. The analyst does not care about punctuation (remove_punct =TRUE)! The analyst has to reduce the burden of his word sack, and there he or she notices the "stopwords", functional words that connect and move ideas as words .. "then" and "when", "how" and "because". Any contents? No! Throw them away! It might be true that programs for Data Analysis have been developed for, and are mostly used by marketing experts who do not really care about moving ideas, and usually end up with some "sentiment analysis": Feelin good? Ok! It is also true that in AI, things like Natural Language Understanding, caution is the dominant rule. Keep the stopwords, you never know! There are good reasons for doing so. AI should include every word that touches huma

  When we take a look at the most frequent words with a wordcloud (wordmax =20), having eliminated the stopwords, the most evident ones describe elementary states: "Lust", "Kälte", "Licht", "Mann"  Within this list, "Liebe" could be surprising, being or not being an elementary state? "Herz" of symbolic quality. "America" only appears in one song. Numerically, it looks like this: feature frequency docfreq      relative frequency   relative ranking 1 liebe 46                   11                     0.63428098                    1 2 mann 45                 11                       0.46447375                    6 3 gut       44                 18                      0.48527064                    4 4 komm 43                 9 5 ja        43                 12  6 immer 42                19                      0.50531695                    3 7 sonne 42                 15                      0.45047007 8 nacht 41        

In Italy, in Germany, in Poland.  Teaching German can all of a sudden seem exciting when we listen to Rammstein in class. Some students, as always, do not care, but there are only few of them. Some will be repelled, but a major group will start nodding and shouting as they start understanding the texts of songs they have always, at least in a certain period of their live, listened to.  When "du hasst" turns to "du hast mich gefragt", and back again, there is a light in the classroom. You may not love the industrial hammering of the music, you may be shocked by hearing aggressive German shouting, but 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? Isolate motifs and topics? See how they work? I want to try to make them read by the machine. The programming languages R and Pytho