Text classification with Naïve Bayes

https://doi.org/10.65136/jati.v5i1.210
Naive Bayes algorithm text classification Multinomial Naive Bayes (MNB)

Authors

  • Yap Chi Yew School of Computing Asia Pacific University of Technology and Innovation (APU) Kuala Lumpur, Malaysia
  • Tan Guan Cheng School of Computing Asia Pacific University of Technology and Innovation (APU) Kuala Lumpur, Malaysia
  • Derric Chong Wei Sen School of Computing Asia Pacific University of Technology and Innovation (APU) Kuala Lumpur, Malaysia
  • Bee Jian Wei School of Computing Asia Pacific University of Technology and Innovation (APU) Kuala Lumpur, Malaysia
  • Zailan Arabee Abdul Salam
    zailan@apu.edu.my
    School of Computing Asia Pacific University of Technology and Innovation (APU) Kuala Lumpur, Malaysia https://orcid.org/0009-0003-4288-0843
Vol. 5 No. 2 (2021)
Original Research
January 27, 2026

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Naive Bayes is an algorithm which is swift and easy to apply and always used in text classification. In this paper, we anchor in the performance of Naive Bayes text classifiers with a couple of datasets. We have modified the code to measure the accuracy of the algorithm in predicting words. We also use an interactive graph to show the prediction of the algorithm. We found out that Naive Bayes can provide uncomplicated probabilistic predictions which are very smoothly interpretable with only a few tuneable parameters.