title: Classifying text documents using nltk
body: <p>I'm currently working on a project where I'm taking emails, stripping out the message bodies using the email package, then I want to categorize them using labels like sports, politics, technology, etc...</p>
  
  <p>I've successfully stripped the message bodies out of my emails, now I'm looking to start classifying. I've done the classic example of sentiment-analysis classification using the move_reviews corpus separating documents into positive and negative reviews.</p>
  
  <p>I'm just wondering how I could apply this approach to my project? Can I create multiple classes like sports, technology, politics, entertainment, etc.? I have hit a road block here and am looking for a push in the right direction.</p>
  
  <p>If this isn't an appropriate question for SO I'll happily delete it.</p>
  
  <p><strong>Edit</strong>: Hello everyone, I see that this post has gained a bit of popularity, I did end up successfully completing this project, here is a link to the code in the projects GitHub Repo:
  <a href="https://github.com/codyreandeau/Email-Categorizer/blob/master/Email_Categorizer.py" rel="nofollow noreferrer">https://github.com/codyreandeau/Email-Categorizer/blob/master/Email_Categorizer.py</a></p>