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I'm using python and I want to get the TFIDF representation for a large corpus of data, I'm using the following code to convert the docs into their TFIDF form.
from sklearn.feature_extraction.text import TfidfVectorizer
tfidf_vectorizer = TfidfVectorizer(
min_df=1, # min count for relevant vocabulary
max_features=4000, # maximum number of features
strip_accents='unicode', # replace all accented unicode char
# by their corresponding ASCII char
analyzer='word', # features made of words
token_pattern=r'\w{1,}', # tokenize only words of 4+ chars
ngram_range=(1, 1), # features made of a single tokens
use_idf=True, # enable inverse-document-frequency reweighting
smooth_idf=True, # prevents zero division for unseen words
sublinear_tf=False)
tfidf_df = tfidf_vectorizer.fit_transform(df['text'])
Here I pass a parameter max_features. The vectorizer will select the best features and return a scipy sparse matrix. Problem is I dont know which features are getting selected and how do I map those feature names back to the scipy matrix I get? Basically for the n selected features from the m number of documents, I want a m x n matrix with the selected features as the column names instead of their integer ids. How do I accomplish this?
635
You can use tfidf_vectorizer. get_feature_names() . This will print feature names selected (terms selected) from the raw documents. You can also use tfidf_vectorizer.
TF-IDF is better than Count Vectorizers because it not only focuses on the frequency of words present in the corpus but also provides the importance of the words. We can then remove the words that are less important for analysis, hence making the model building less complex by reducing the input dimensions.
Term frequency-inverse document frequency is a text vectorizer that transforms the text into a usable vector. It combines 2 concepts, Term Frequency (TF) and Document Frequency (DF). The term frequency is the number of occurrences of a specific term in a document.
From the way the TfIdf score is set up, there shouldn't be any significant difference in removing the stopwords. The whole point of the Idf is exactly to remove words with no semantic value from the corpus. If you do add the stopwords, the Idf should get rid of it.
When using the TfidfVectorizer with max_features=N (where N is not None ), I would expect the algorithm to sort by the tfidf score and then take the top N features. Instead, it sorts by document frequency.
TFIDF Vectorizer. In simple words, TFIDF is a numerical… | by Karan Arya | NLP Gurukool | Medium In simple words, TFIDF is a numerical statistic that shows the importance of a word in a text document. ['I', 'love', 'my', 'cat', 'but', 'the', 'cat', 'sat', 'on', 'my', 'face']
IDF show how common or rare a given word is across all documents. Tf*Idf do not convert directly raw data into useful features. Firstly, it converts raw strings or dataset into vectors and each word has its own vector. Then we’ll use a particular technique for retrieving the feature like Cosine Similarity which works on vectors, etc.
Tf*Idf do not convert directly raw data into useful features. Firstly, it converts raw strings or dataset into vectors and each word has its own vector. Then we’ll use a particular technique for retrieving the feature like Cosine Similarity which works on vectors, etc.
You can use tfidf_vectorizer.get_feature_names(). This will print feature names selected (terms selected) from the raw documents.
You can also use tfidf_vectorizer.vocabulary_ attribute to get a dict which will map the feature names to their indices, but will not be sorted. The array from get_feature_names() will be sorted by index.
156
use tfidf_vectorizer.vocabulary_, this gives a mapping from the features (terms back to the indices)
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