Why in NLTK "not" is considered as stopping word in English?
Stop words are usually created based on analysis of the most common words in the language (that is the main reason "not" is in there). The idea behind them is to allow analysis (normally a bag of words approach) to focus on the more interesting words of the document.
They are used for multiple purposes: they were not designed specifically for sentiment analysis. That is another reason "not" is in there.
A bag-of-words approach to sentiment analysis is always going to end up a bit crude, with or without stop words. E.g. It is not hard to create realistic-sounding positive movie reviews using negative words:
"This movie was not at all what I expected: I usually hate Tom Cruise but he made this movie work."
"I never watch chick-flicks, but as they say, never say never."
Anyway try customizing your stopword list, removing "not" and "never" to see if it improves results. Then try again using no stopwords at all.
I am currently learning nltk in Python where I am writing a program for sentiment analysis. While working on it, I found out that "not, nor, never" are considered stopping words. So my question is why that is because these kinds of words can change the entire meaning of sentences and could lead to failure in sentiment analysis.