I'm building a text classifier using the naive Bayes formula. I'm still early in the development, but I already see a problem with my technique and I was wondering if you guys would have idea that would help me solve this problem.
What I want to do is score texts to order them from the more likely to be in class A to the less likely to be. I only have one class and I want to find the likelyhood that a text is in it.
The problem is I only get prediction really near zero (1,068E-12 for exemple). the reason is most words have a probability of being in class A inferior to 0.5. Even if I have words with probabilities > 0.5, theses probabilities are farther from 1 then the probabilities <0.5 are farther from 0.
So when I choose the N words with the probabilities the farthest from .05 I usually get only (or at least more) probabilities <0.5. And so the more words I use (N) the more the probability is near 0.
Is there some optimization I could implement that would help with this problem (For now I don't even remove stop words, but I plan to)?
Or is a Bayes classifier a bad choice for my problem?