# Fast and accurate sentiment classification using an enhanced Naive Bayes model

arXiv: https://arxiv.org/abs/1305.6143 · HTML: https://mlsanity.com/paper/1305.6143/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 May 2013.

## Built on by (most cited first, 3 of 3)

- [Deep Neural Network Based Ensemble learning Algorithms for the healthcare system (diagnosis of chronic diseases)](https://mlsanity.com/paper/2103.08182/) (2021)
- [Creating emoji lexica from unsupervised sentiment analysis of their descriptions](https://mlsanity.com/paper/2404.01439/) (2024)
- [A sentiment analysis model for car review texts based on adversarial training and whole word mask BERT](https://mlsanity.com/paper/2206.02389/) (2022)

## Abstract (arXiv)

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.