natural language processing - How to interpret TfidfVectorizer output

I am doing sentiment analysis and for feature generation from text, I am using TF-IDF method but I am not able interpret the output.I have used the TfidfVectorizer function from Sklearn.I have used the below code:from sklearn.feature_extraction.text import TfidfVectorizertfidf_vectorizer = TfidfVectorizer(max_df=0.90, min_df=2, max_features=1000, stop_words='english')tfidf = tfidf_vectorizer.fit_transform(combi['tidy_tweet'])The output is below:(0, 302) 0.46871135687055143 (0, 463) 0.5896490179849546 (0, 738) 0.6577413621857342 (1, 879) ...Read more

tfidfvectorizer - Need to create dictionary of idf values, associating words with their idf values

I understand how to get the idf values and vocabulary using the vectorizer. With vocabulary the frequency of the word is the value and the word is the key of a dictionary, however, what I want the value to be is the idf value. I haven't been able to try anything because I don't know how to work with sklearn.from sklearn.feature_extraction.text import TfidfVectorizer# list of text documentstext = ["The quick brown fox jumped over the lazy dog.", "The dog.", "The fox"]# create the transformvectorizer = TfidfVectorizer()# tokenize an...Read more