Smoothing in python NLTK
I'd suggest to replace all the words with low (specially 1) frequency to <unseen>, then train the classifier in this data. 
For classifying you should query the model for <unseen> in the case of a word that is not in the training data.
I am using Naive Bayes classifier in python for text classification. Is there any smoothing methods to avoid zero probability for unseen words in python NLTK? Thanks in advance!