Algorithm to determine how positive or negative a statement/text is

Question

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.

For instance:

<

Answer 1

Maybe essay grading software could be used to estimate tone? WIRED article.
Possible reference. (I couldn't read it.)
This report compares writing skill to the Flesch-Kincaid Grade Level needed to read it!
Page 4 of e-rator says that they look at mispelling and such. (Maybe bad post are misspelled too!)
Slashdot article.

You could also use an email filter of some sort for negativity instead of spam-ness.

Answer 2

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.

Answer 3

It's all about context, I think. If you're looking for the people who are best at sucking with SO. Sucking the best can be a positive thing. For determination what is bad or good and how much I could recommend looking into Fuzzy Logic.

It's a bit like being tall. Someone who's 1.95m can considered to be tall. If you place that person in a group with people all over 2.10m, he looks short.

Answer 4

How about sarcasm:

• Jason is the best SO user I have ever seen, NOT
• Jason is the best SO user I have ever seen, right

Answer 5

As pointed out, this comes under sentiment analysis under natural language processing.
Afaik GATE doesn't have any component that does sentiment analysis.
In my experience, I have implemented an algorithm which is an adaptation of the one in the paper 'Recognizing Contextual Polarity in Phrase-Level Sentiment Analysis' by Theresa Wilson, Janyce Wiebe, Paul Hoffmann (this) as a GATE plugin, which gives reasonable good results. It could help you if you want to bootstrap the implementation.

Answer 6

   use Algorithm::NaiveBayes;
     my $nb = Algorithm::NaiveBayes->new;

     $nb->add_instance
       (attributes => {foo => 1, bar => 1, baz => 3},
        label => 'sports');

     $nb->add_instance
       (attributes => {foo => 2, blurp => 1},
        label => ['sports', 'finance']);

     ... repeat for several more instances, then:
     $nb->train;

     # Find results for unseen instances
     my $result = $nb->predict
       (attributes => {bar => 3, blurp => 2});