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Implementation and Comparison of Deep Learning, and Naïve Bayes for Language Processing

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  • Abiodun Olalere

    (Department of Computer Science, ESPAM Formation University,Cotonou, Benin)

Abstract

Text classification is one of the most important tasks in natural language processing, in this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN work best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence.

Suggested Citation

  • Abiodun Olalere, 2024. "Implementation and Comparison of Deep Learning, and Naïve Bayes for Language Processing," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 11(2), pages 545-552, February.
    • Handle: RePEc:bjc:journl:v:11:y:2024:i:2:p:545-552
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