Abstract :

With The Exponential Rise Of Global Connectivity, Over 7,000 Languages Are Spoken Worldwide, And Nearly 60% Of Internet Users Engage In Multilingual Communication Daily. Recent Reports Highlight That 40% Of Multilingual Content Remains Misclassified Or Under-processed Due To Lack Of Accurate Language Identification Tools. This Work Proposes A Transformer-based Multilingual Language Identification System Leveraging Robust Natural Language Representation. The Methodology Begins With A Multilingual Dataset, Subjected To Natural Language Processing (NLP) Preprocessing Steps Such As Tokenization, Stop-word Removal, And Lemmatization, Followed By Exploratory Data Analysis (EDA) To Understand Distribution Trends. Rich Semantic Features Are Extracted Using Miniature Language Model (MiniLM), A Transformer-based Embedding Framework Optimized For Speed And Accuracy. For Baseline Comparison, Traditional Classifiers, Including Decision Tree Classifier (DTC), K-Nearest Neighbors (KNN), And Gaussian Naïve Bayes Classifier (GNB), Are Tested. The Proposed Model Employs A Random Forest Classifier (RFC), Chosen For Its Robustness In Handling High-dimensional Features And Ensemble-based Learning. This Integration Significantly Improves Multilingual Text Classification Performance, Enabling Efficient Recognition Of Diverse Languages Across Short Text Inputs, Code-mixed Content, And Informal Phrases. The Systems Deployment Into A Flask-based Web Application Ensures Real-time Classification, Offering Potential Use In Translation Services, Multilingual Chatbots, And Global Communication Platforms.

Published:

24-4-1-2026

Issue:

Vol. 26 No. 4-1 (2026)

Page Nos:

868-877

Section:

Articles

License:

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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