Text PreProcessing not improving accuracy
You can find that information on the class's documentation, here's the link.

Quick summary:


lowercasing: True by default.
lemmatization: I think the closest parameter is preprocessor; it's
set to None by default but you can specify as the value a callable
object of your own and then the vectorizer will apply it on every
word to do the lemmatization, e.g.NLTK's WordNet lemmatizer.
tokenizer: None by default (because the class expects its input to
be already tokenized words, so no need for this unless that's not
so).
stopwords: None by default (but you can specify a list of strings as
the argument and then the class will filter out any words in that
list).


I agree that it is suspicious that you're getting the same Accuracy, generally it is expected to go up when using TFIDF. Make sure you're

1) sending the right input to the TfIdfVectorizer class (it takes a list: list: str, dataset: sentence: each word),

2) using its output (perhaps some variable in your code is still pointing to the non-TFIDF representation instead of the TFIDF one?)

I think it would not be theoretically impossible for SGDClassifier to approximate TFIDF behavior over a sufficiently large number of training iterations with the right learning rate (if all the stopwords' weights are effectively minimized while optimizing loss, TFIDF might have little effect at that point). It would be good to know what parameters you're using and/or to see the code to help you pinpoint the issue.