title: ValueError: Iterable over raw text documents expected, string object received. Predicting new test data with tfidf and selection feature
body: <p>So i have build a model with sklearn Naive Bayes classifier.
  I need to know how to predict a sentence with input</p>
  
  <p>when i just hardcode the sentence its work fine, look like this</p>
  
  <pre><code>new_sentence = ['its so broken']
  new_testdata_tfidf= tfidf.transform(new_sentence) 
  #transform it to matrix to see the score TFIDF on the training data
  fit_feature_selection = selection.transform(new_testdata_tfidf) 
  #transform the new data to see if the feature remove or not, because after tfidf i use chi2 selection feature.
  predicted = classifier.predict(feature_selection )
  #then predict it. the classificaiton out, the class is -1 which is the correct answer
  </code></pre>
  
  <p>i need to type the text data with hand as <strong>an input</strong> so I use like this</p>
  
  <pre><code>new_sentence = input[('')] 
  #i input the same sentence its so broken 
  new_testdata_tfidf= tfidf.transform(new_sentence) 
  #transform it to matrix to see the score TFIDF on the training data
  fit_feature_selection = selection.transform(new_testdata_tfidf) 
  #transform the new data to see if the feature remove or not, because after tfidf i use chi2 selection feature.
  predicted = classifier.predict(feature_selection )
  </code></pre>
  
  <p>but it give me output</p>
  
  <pre><code>  File "C:\Users\Myfile\OneDrive\Desktop\model.py", line 170, in &lt;module&gt;
     new_testdata_tfidf= tfidf.transform(new_sentence) 
  
    File "E:\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 1898, in transform
      X = super().transform(raw_documents)
  
    File "E:\anaconda3\lib\site-packages\sklearn\feature_extraction\text.py", line 1265, in transform
      "Iterable over raw text documents expected, "
  
  ValueError: Iterable over raw text documents expected, string object received.
  </code></pre>
  
  <p>How to resolve this?
  any help really appreciated.</p>