print(tfidf(" "abbd) print(tfidf(" "abcd) print(tfidf(" "abcd) print(tfidf(" "abcd) we see that carries no meaning for any document since it is contained everywhere is more important for the document abb than for abc as it occurs there twice in realitythere are more corner cases to handle than the above example does thanks to scikitwe don' have to think of themas they are already nicely packaged in tfidfvectorizerwhich is inherited from countvectorizer sure enoughwe don' want to miss our stemmerfrom sklearn feature_extraction text import tfidfvectorizer class stemmedtfidfvectorizer(tfidfvectorizer)def build_analyzer(self)analyzer super(tfidfvectorizerselfbuild_analyzer(return lambda docenglish_stemmer stem(wfor in analyzer(doc)vectorizer stemmedtfidfvectorizer(min_df= stop_words='english'charset_error='ignore'the resulting document vectors will not contain counts any more insteadthey will contain the individual tf-idf values per term our achievements and goals our current text preprocessing phase includes the following steps tokenizing the text throwing away words that occur way too often to be of any help in detecting relevant posts