title: How to predict the topic of a new query using a trained LDA model using gensim?
body: <p>I have trained a corpus for LDA topic modelling using gensim.</p>
  
  <p>Going through the tutorial on the gensim website (this is not the whole code):</p>
  
  <pre><code>question = 'Changelog generation from Github issues?';
  
  temp = question.lower()
  for i in range(len(punctuation_string)):
      temp = temp.replace(punctuation_string[i], '')
  
  words = re.findall(r'\w+', temp, flags = re.UNICODE | re.LOCALE)
  important_words = []
  important_words = filter(lambda x: x not in stoplist, words)
  print important_words
  dictionary = corpora.Dictionary.load('questions.dict')
  ques_vec = []
  ques_vec = dictionary.doc2bow(important_words)
  print dictionary
  print ques_vec
  print lda[ques_vec]
  </code></pre>
  
  <p>This is the output that I get:</p>
  
  <pre><code>['changelog', 'generation', 'github', 'issues']
  Dictionary(15791 unique tokens)
  [(514, 1), (3625, 1), (3626, 1), (3627, 1)]
  [(4, 0.20400000000000032), (11, 0.20400000000000032), (19, 0.20263215848547525), (29, 0.20536784151452539)]
  </code></pre>
  
  <p>I don't know how the last output is going to help me find the possible topic for the <code>question</code> !!!</p>
  
  <p>Please help!</p>