<p>I need an algorithm to determine if a sentence, paragraph or article is negative or positive in tone... or better yet, how negative or positive.</p>

<p>For instance:</p>

<blockquote>
  <blockquote>
    <p>Jason is the worst SO user I have ever witnessed (-10)</p>
    
    <p>Jason is an SO user (0)</p>
    
    <p>Jason is the best SO user I have ever seen (+10)</p>
    
    <p>Jason is the best at sucking with SO (-10)</p>
    
    <p>While, okay at SO, Jason is the worst at doing bad (+10)</p>
  </blockquote>
</blockquote>

<p>Not easy, huh? :)</p>

<p>I don't expect somebody to explain this algorithm to me, but I assume there is already much work on something like this in academia somewhere. If you can point me to some articles or research, I would love it.</p>

<p>Thanks.</p>

This falls under the umbrella of
Natural Language Processing
, and so reading about that is probably a good place to start.
If you don't want to get in to a very complicated problem, you can just create lists of "positive" and "negative" words (and weight them if you want) and do word counts on sections of text.  Obviously this isn't a "smart" solution, but it gets you some information with very little work, where doing serious NLP would be very time consuming.
One of your examples would potentially be marked positive when it was in fact negative using this approach ("Jason is the best at sucking with SO") unless you happen to weight "sucking" more than "best"....  But also this is a small text sample, if you're looking at paragraphs or more of text, then weighting becomes more reliable unless you have someone purposefully trying to fool your algorithm.