title: Best Algorithmic Approach to Sentiment Analysis
body: <p>My requirement is taking in news articles and determining if they are positive or negative about a subject.  I am taking the approach outlined below, but I keep reading NLP may be of use here.  All that I have read has pointed at NLP detecting opinion from fact, which I don't think would matter much in my case. I'm wondering two things:</p>
  
  <p>1)  Why wouldn't my algorithm work and/or how can I improve it? ( I know sarcasm would probably be a pitfall, but again I don't see that occurring much in the type of news we will be getting)</p>
  
  <p>2)  How would NLP help, why should I use it?</p>
  
  <p>My algorithmic approach (I have dictionaries of positive, negative, and negation words):</p>
  
  <p>1) Count number of positive and negative words in article</p>
  
  <p>2) If a negation word is found with 2 or 3 words of the positive or negative word, (ie: NOT the best) negate the score.</p>
  
  <p>3) Multiply the scores by weights that have been manually assigned to each word. (1.0 to start)</p>
  
  <p>4) Add up the totals for positive and negative to get the sentiment score.</p>