title: Sentiment Analysis on LARGE collection of online conversation text
body: <p>The title says it all; I have an SQL database bursting at the seams with online conversation text. I've already done most of this project in Python, so I would like to do this using Python's NLTK library (unless there's a <strong>strong</strong> reason not to).</p>
  
  <p>The data is organized by <strong>Thread</strong>, <strong>Username</strong>, and <strong>Post</strong>. Each thread more or less focuses on discussing one "product" of the Category that I am interested in analyzing. Ultimately,  when this is finished, I would like to have an estimated opinion (like/dislike sort of deal) from each user for any of the products they had discussed at some point.</p>
  
  <p>So, what I would like to know:</p>
  
  <p>1) <strong>How can I go about determining what product each thread is about?</strong> I was reading about keyword extraction... is that the correct method?</p>
  
  <p>2) <strong>How do I determine a specific users sentiment based on their posts?</strong> From my limited understanding, I must first "train" NLTK to recognize certain indicators of opinion, and then do I simply determine the context of those words when they appear in the text?</p>
  
  <p>As you may have guessed by now, I have no prior experience with NLP. From my reading so far, I think I can handle learning it though. Even just a basic and crude working model for now would be great if someone can point me in the right direction. Google was not very helpful to me.</p>
  
  <p><strong>P.S.</strong> I have permission to analyze this data (in case it matters)</p>