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arXiv:1305.6143cs.CL

Fast and accurate sentiment classification using an enhanced Naive Bayes model

Vivek Narayanan, Ishan Arora, Arjun Bhatia

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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 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.

arXiv comments
8 pages, 2 figures
Journal reference
Intelligent Data Engineering and Automated Learning IDEAL 2013 Lecture Notes in Computer Science Volume 8206, 2013, pp 194-201
DOI
doi:10.1007/978-3-642-41278-3_24
Subjects
cs.CLComputation and Language cs.IRInformation Retrieval cs.LGMachine Learning

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  1. Ask HN: Sentiment Analysis – how to handle biased word list lengths? 1 comment

    • I found this paper useful for a side project I worked on a few months ago, one that made use of n-grams in a naive bayesian classifier: http://arxiv.org/pdf/1305.6143v2.pdf and the lead authors's github repos are: https://github.com/vivekn/sentiment…

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