title: NLP Negation detection and Stop Words
body: <p>I'd like to improve my sentiment analysis via negation detection. I'm implementing sentiment analysis using a bag of words approach highlight by professor, hence why I'm not using CoreNLP's sentiment annotator, yet. However, I noticed it has a problem.</p>
  
  <p>Given the sentence, "I'm not disappointed in them", I would expect, at worst, a neutral sentiment, or weakly positive sentiment, both from the sentiment annotator, and my own bag of words implementation. The sentiment annotator reports this sentence as negative.</p>
  
  <pre><code>I: PRP  Neutral
  'm: VBP Neutral
  not: RB Negative
  disappointed: VBN   Negative
  in: IN  Neutral
  them: PRP   Neutral
  .: .    Neutral
  Negative
  1
  </code></pre>
  
  <p>The last two lines show the sentence's sentiment label and the numerical sentiment score.</p>
  
  <p>How do I improve the sentiment annotator's chances of getting this right, and how can I use CoreNLP to detect negation like what's shown, negation across sentences, and references to an entity across multiple sentences (which seems like the coref and dcoref annotators)?</p>
  
  <p>Also, potentially useful would be getting rid of stop words. The lemma annotator seems to take care of stemming, but which annotator does stop words?</p>