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Erschienen in:
2013 | OriginalPaper | Buchkapitel
Fast and Accurate Sentiment Classification Using an Enhanced Naive Bayes Model
verfasst von : Vivek Narayanan, Ishan Arora, Arjun Bhatia
Erschienen in: Intelligent Data Engineering and Automated Learning – IDEAL 2013
Verlag: Springer Berlin Heidelberg
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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.