Earlier quoted context omitted.
Have you tried Spacy? I find it substantially better than other tools as PoS tagger. Also worth noting the that your assertion that you need these features to classify genres isn't obviously true to me at all.
No I haven't. Thanks for the pointer. For detecting uses of nouns like werewolf/werewolves, or vampire/vampires, I at least need the lemma to avoid writing different cases or a regex for each noun. Likewise, lemmatization can be used to handle different spellings (e.g. vampyre, or were-wolf). Similarly for verbs. Lemmatization works best when it is coupled with part of speech tagging, so you avoid removing the -ing i…
But I did a lot of work on this type of thing and the only time I found this sentence analysis approach was useful as classifier features was in a legal context where there were variants of very specific language we wanted to find.
There it worked because we could write rules on the features without relying on training data.
Tf-idf on ngrams using a rolling window would certainly work to detect the beheading variants you gave as examples.
Again: try without the parsing features. There's a good reason they are rarely used in classifiers: they are too unreliable to improve performance over simple approaches.