Text News Classification System using Naive Bayes Classifier

This paper describes the Naive Bayes text News classification system developed for Punjabi Language. News corpus is used for training and testing purpose of the classifiers. Language specific preprocessing techniques are applied on raw data to generate a standardized and reduced-feature lexicon. Punjabi language is morphological rich language which makes those tasks complex. Statistical characteristics of corpus and lexicon are measured which show satisfactory results of text preprocessing module. We are able to get satisfactory results using Naive Bayes Classifier.