title: bag of words model not making sense
body: <p>I made a bag of words model and when I printed it out, the output is not quite making sense. 
  This is my code I used to initalise the bag of words: </p>
  
  <pre><code>#creating the bag of words model
  
  headline_bow = CountVectorizer()
  headline_bow.fit(x)
  a = headline_bow.transform(x)
  b = headline_bow.get_feature_names()
  print(a)
  </code></pre>
  
  <p>This is a sample of the output that comes out from the bag of words model: </p>
  
  <pre><code>  (0, 837)  1
    (0, 1496) 1
    (0, 1952) 1
    (0, 2610) 1 
  </code></pre>
  
  <p>From my understanding, for "(0, 837) 1" this means that in the first list passed through the model, the 837th word in that list appears once. This makes no sense because when I print 
  x[0] I get this: </p>
  
  <pre><code>Four ways Bob Corker skewered Donald Trump
  </code></pre>
  
  <p>There is clearly not 837 words here so im confused as to whats going on. </p>
  
  <p>Here is a sample of what x is: (a bunch of headlines) </p>
  
  <pre><code>['Four ways Bob Corker skewered Donald Trump'
   "Linklater's war veteran comedy speaks to modern America, says star"
   'Trump’s Fight With Corker Jeopardizes His Legislative Agenda' ...
   'Ron Paul on Trump, Anarchism &amp; the AltRight'
   'China to accept overseas trial data in bid to speed up drug approvals'
   'Vice President Mike Pence Leaves NFL Game Because of Anti-American Protests']
  </code></pre>
  
  <p>Here is the rest of my code: </p>
  
  <pre><code>data = pd.read_csv("/Users/amanpuranik/Desktop/fake-news-detection/data.csv")
  data = data[['Headline', "Label"]]
  
  x = np.array(data['Headline'])
  print(x[0])
  y = np.array(data["Label"])
  
  # tokenization of the data here'
  headline_vector = []
  
  for  headline in x:
      headline_vector.append(word_tokenize(headline))
  
  print(headline_vector)
  
  stopwords = set(stopwords.words('english'))
  
  #removing stopwords at this part
  filtered = [[word for word in sentence if word not in stopwords]
              for sentence in headline_vector]
  #print(filtered)
  
  
  stemmed2 = [[stem(word) for word in headline] for headline in filtered]
  #print(stemmed2)
  
  #lowercase
  lower = [[word.lower() for word in headline] for headline in stemmed2] #start here
  
  #conver lower into a list of strings
  lower_sentences = [" ".join(x) for x in lower]
  
  #organising
  articles = []
  
  
  for headline in lower:
      articles.append(headline)
  
  #creating the bag of words model
  
      headline_bow = CountVectorizer()
  headline_bow.fit(lower_sentences)
  a = headline_bow.transform(lower_sentences)
  print(a)
  </code></pre>