Fast and accurate sentiment classification using an enhanced Naive Bayes model
This work provides a fast and accurate sentiment classifier for text analysis applications, though it is incremental as it builds on existing Naive Bayes methods.
The authors tackled sentiment classification by enhancing a Naive Bayes model with techniques like negation handling and feature selection, achieving an accuracy of 88.80% on the IMDB movie reviews dataset.
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.