document classification using naive bayes in python
There could be many reasons for the classifier not working, and there are many ways to tweak it.

did you train it with enough positive and negative examples?
how did you train the classifier? did you give it every word as a feature, or did you also add more features for it to train on(like length of the text for example)?
what exactly are you trying to classify? does the specified classification have specific words that are related to it?

So the question is rather broad. Maybe If you give more details You could get more relevant suggestions.
I'm doing a project on document classification using naive bayes classifier in python. I have used the nltk python module for the same. The docs are from reuters dataset. I performed preprocessing steps such as stemming and stopword elimination and proceeded to compute tf-idf of the index terms. i used these values to train the classifier but the accuracy is very poor(53%). What should I do to improve the accuracy?