TITLE:
Comparative Analysis of Neural Networks and Naive Bayes for Multilingual Text Identification
AUTHORS:
Xinyu Zhang, Chaoya Yan, Jiaqing Shen
KEYWORDS:
Language Identification, Character-Level Neural Networks, Naive Bayes, Multilingual Text Classification, CNN/RNN, Character N-Grams, Computational Efficiency, Performance Analysis, Text Processing, Machine Learning, Natural Language Processing, Comparative Study
JOURNAL NAME:
Journal of Software Engineering and Applications,
Vol.18 No.11,
November
27,
2025
ABSTRACT: This study presents a comparative analysis of two distinct machine learning approaches for multilingual text identification: character-level neural networks (CNN/RNN) and traditional Naive Bayes classifiers. We constructed a dataset comprising 20 languages, including Arabic, Bulgarian, German, Greek, English, Spanish, French, Hindi, Italian, Japanese, Korean, Dutch, Polish, Portuguese, Russian, Swahili, Thai, Turkish, Urdu, and Vietnamese. Experimental results demonstrate that the character-level neural network model achieved 98.76.