Python: linguistic normalization
This is a bit more experimental, but another possibility is to use word embeddings.
The words great and good should have similar occurrence contexts, so their vectors should be similar, you cluster your words like that, and aggregate them into the same word/concept.
Of course this will greatly depend on the corpus and methods you use to generate the embeddings.
I'm looking for a lemmatization module/lib that will transfer a sentence like:
 "this is great" to "this is good".
I'm familiar with some of the tools available in nltk such as stemming and lemmatization, however it's not exactly what I'm looking for
My goal is to minimize the variety of ways saying the same thing.