Text Classification by Enhancing Weights of Terms based on their Positional Appearances

Abstract

store of hidden information in text documents is available. Extracting accurate, useful information from this store is very important. Multinomial Naive Bayes classification algorithm is effective in processing text and extracting accurate information. A new approach of assigning weights to terms based on their positional appearance is proposed. The effectiveness of this approach is demonstrated for two standard text datasets Reuters-21578 and 20-newsgroups. This proposed approach improves average F-measure by 1.0% for Reuters-21578 and by 2% for 20-newsgroups at least.