Abstract
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 effective 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. The proposed method can be generalized to a number of text categorization problems for improving speed and accuracy.
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References
Large Movie Review Dataset (n.d.), http://ai.stanford.edu/~amaas/data/sentiment/
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Narayanan, V., Arora, I., Bhatia, A. (2013). Fast and Accurate Sentiment Classification Using an Enhanced Naive Bayes Model. In: Yin, H., et al. Intelligent Data Engineering and Automated Learning – IDEAL 2013. IDEAL 2013. Lecture Notes in Computer Science, vol 8206. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-41278-3_24
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DOI: https://doi.org/10.1007/978-3-642-41278-3_24
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