title: vectorizer my own text data
body: <p>I am trying to vectorize some text documents that I have.
  Preparing it for a fit() method.</p>
  
  <p>I did quite a bit of search on this and based on all that I saw looks like this is the right code</p>
  
  <pre><code>from sklearn.feature_extraction.text import TfidfVectorizer
  
  filenames = ['aa.txt','bb.txt','cc.txt']
  
  vectorizer = TfidfVectorizer()
  
  X = vectorizer.fit_transform(filenames)
  
  print ("feature names - ")
  print (vectorizer.get_feature_names())
  </code></pre>
  
  <p>I was expecting to see the words within the files as my feature names
  But instead here is what I see</p>
  
  <pre><code>feature names - 
  ['aa', 'bb', 'cc', 'txt']
  </code></pre>
  
  <p>I can see this from the documentation - "If ‘filename’, the sequence passed as an argument to fit is expected to be a list of filenames that need reading to fetch the raw content to analyze. "</p>