Papers › Fast and accurate sentiment classification using an enhanced Naive Bayes model
Fast and accurate sentiment classification using an enhanced Naive Bayes model
Vivek Narayanan, Ishan Arora, Arjun Bhatia
We have explored different methods of improving the accuracy of a Naive Bayes classifier for sentiment analysis. We observed that a combination of methods like negation handling, word n-grams and feature selection by mutual information results in a significant improvement in accuracy. This implies that a highly accurate and fast sentiment classifier can be built using a simple Naive Bayes model that has linear training and testing time complexities. We achieved an accuracy of 88.80% on the popular IMDB movie reviews dataset.
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