tfidfvectorizer

问题 I want to extract the ngrams of the tweets, from two groups of users (0/1), to make a CSV file as follows for a binary classifier. user_tweets, ngram1, ngram2, ngram3, ..., label 1, 0.0, 0.0, 0.0, ..., 0 2, 0.0, 0.0, 0.0, ..., 1 .. My question is whether I should first extract the important ngrams of the two groups, and then score each ngram that I found in the user's tweets? or is there an easier way to do this? 来源： https://stackoverflow.com/questions/66092089/binary-classification-using-the

问题 How can I check the strings tokenized inside TfidfVertorizer() ? If I don't pass anything in the arguments, TfidfVertorizer() will tokenize the string with some pre-defined methods. I want to observe how it tokenizes strings so that I can more easily tune my model. from sklearn.feature_extraction.text import TfidfVectorizer corpus = ['This is the first document.', 'This document is the second document.', 'And this is the third one.', 'Is this the first document?'] vectorizer = TfidfVectorizer

问题 I was trying to know the tf-idf scores of each word in my document. However, it only returns values in the matrix but I see a specific type of representation of tf-idf scores against each word. I have used processed and the code works however I want to change the way it is presented: code: from sklearn.feature_extraction.text import CountVectorizer from sklearn.feature_extraction.text import TfidfTransformer bow_transformer = CountVectorizer(analyzer=text_process).fit(df["comments"].head())

来源： https://stackoverflow.com/questions/64442720/re-calculate-similarity-matrix-given-new-documents

来源： https://stackoverflow.com/questions/64442720/re-calculate-similarity-matrix-given-new-documents

问题 Sklearn does few tweaks in the implementation of its version of TFIDF vectorizer, so to replicate the exact results you would need to add following things to your custom implementation of tfidf vectorizer: Sklearn has its vocabulary generated from idf sroted in alphabetical order Sklearn formula of idf is different from the standard textbook formula. Here the constant "1" is added to the numerator and denominator of the idf as if an extra document was seen containing every term in the

问题 I have a list of tokenized sentences and would like to fit a tfidf Vectorizer. I tried the following: tokenized_list_of_sentences = [['this', 'is', 'one'], ['this', 'is', 'another']] def identity_tokenizer(text): return text tfidf = TfidfVectorizer(tokenizer=identity_tokenizer, stop_words='english') tfidf.fit_transform(tokenized_list_of_sentences) which errors out as AttributeError: 'list' object has no attribute 'lower' is there a way to do this? I have a billion sentences and do not want to

问题 I have been running the TF-IDF Vectorizer from SKLearn but am having trouble recreating the values manually (as an aid to understanding what is happening). To add some context, i have a list of documents that I have extracted named entities from (in my actual data these go up to 5-grams but here I have restricted this to bigrams). I only want to know the TF-IDF scores for these values and thought passing these terms via the vocabulary parameter would do this. Here is some dummy data similar

问题 In scikit-learn TfidfVectorizer allows us to fit over training data, and later use the same vectorizer to transform over our test data. The output of the transformation over the train data is a matrix that represents a tf-idf score for each word for a given document. However, how does the fitted vectorizer compute the score for new inputs? I have guessed that either: The score of a word in a new document computed by some aggregation of the scores of the same word over documents in the

问题 TfidfVectorizer provides an easy way to encode & transform texts into vectors. My question is how to choose the proper values for parameters such as min_df, max_features, smooth_idf, sublinear_tf? update: Maybe I should have put more details on the question: What if I am doing unsupervised clustering with bunch of texts. and I don't have any labels for the texts & I don't know how many clusters there might be (which is actually what I am trying to figure out) 回答1: If you are, for instance,