title: How to train large Dataset for classification
body: <p>I have a training dataset of 1600000 tweets. How can I train this type of huge data. </p>
  
  <p>I have tried something using <code>nltk.NaiveBayesClassifier</code>. It will take more than 5 days to train if I run.</p>
  
  <pre><code>def extract_features(tweet):
  
      tweet_words = set(tweet)
      features = {}
      for word in featureList:
          features['contains(%s)' % word] = (word in tweet_words)
  
      return features
  
  
  training_set = nltk.classify.util.apply_features(extract_features, tweets)
  
  NBClassifier = nltk.NaiveBayesClassifier.train(training_set)  # This takes lots of time  
  </code></pre>
  
  <p>What should I do?</p>
  
  <p>I need to classify my Dataset using SVM and naive bayes.</p>
  
  <p>Dataset I want to use : <a href="http://goo.gl/oqStKr" rel="nofollow">Link</a></p>
  
  <p>Sample(training Dataset):</p>
  
  <pre><code>Label     Tweet
  0         url aww bummer you shoulda got david carr third day
  4         thankyou for your reply are you coming england again anytime soon
  </code></pre>
  
  <p>Sample(testing Dataset):</p>
  
  <pre><code>Label     Tweet
  4         love lebron url
  0         lebron beast but still cheering the til the end
  ^
  I have to predict Label 0/4 only
  </code></pre>
  
  <p>How can I train this huge dataset efficiently?</p>