# Naive Bayes and Text Classification I - Introduction and Theory

arXiv: https://arxiv.org/abs/1410.5329 · HTML: https://mlsanity.com/paper/1410.5329/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Oct 2014.

## Built on by (most cited first, 5 of 5)

- [Immersive Text Game and Personality Classification](https://mlsanity.com/paper/2203.10621/) (2022)
- [Identifying COVID-19 Fake News in Social Media](https://mlsanity.com/paper/2101.11954/) (2021)
- [ARTH: Algorithm For Reading Text Handily -- An AI Aid for People having Word Processing Issues](https://mlsanity.com/paper/2101.09464/) (2021)
- [Semantic Code Classification for Automated Machine Learning](https://mlsanity.com/paper/2201.11252/) (2022)
- [Revisiting the Role of Label Smoothing in Enhanced Text Sentiment Classification](https://mlsanity.com/paper/2312.06522/) (2023)

## Abstract (arXiv)

Naive Bayes classifiers, a family of classifiers that are based on the popular Bayes' probability theorem, are known for creating simple yet well performing models, especially in the fields of document classification and disease prediction. In this article, we will look at the main concepts of naive Bayes classification in the context of document categorization.