title: WordCloud.process_text vs sklearn's CountVectorizer
body: <p>I would like to count the term frequency across the corpus. To do that, there are two ways, which was using <code>CountVectorizer</code> and sum in <code>axis=0</code> as below.   </p>
  
  <pre><code>count_vec = CountVectorizer(tokenizer=cab_tokenizer, ngram_range=(1,2), stop_words=stopwords)
  cv_X = count_vec.fit_transform(string_list)
  </code></pre>
  
  <p>Another way is using <code>WordCloud.process_text()</code> (see doc <a href="https://amueller.github.io/word_cloud/generated/wordcloud.WordCloud.html#wordcloud.WordCloud.process_text" rel="nofollow noreferrer">here</a>) which will result in term-frequency <code>dict</code>. I used stopword from previously <code>TfIdfVectorizer</code> using <code>tfidf_vec.get_stop_words()</code>.</p>
  
  <pre><code>text_freq = WordCloud(stopwords=stopwords, collocations=True).process_text(text)
  </code></pre>
  
  <p>The fact that I am using stopwords from the <code>TfIdfVectorizer</code>, I am expecting this to behave the same, however, the features/terms I am getting is different (length of the dict is less than <code>TfIdfVectorizer.get_feature_names()</code>.</p>
  
  <p>So, I am wondering, what is the different of using one over another? Is one more accurate than the other?</p>