Abstract :

With The Rapid Growth Of Social Media Platforms, Vast Amounts Of User-generated Data Are Produced Daily. Twitter, In Particular, Provides Valuable Real-time Information Through Tweets, Which Can Be Analyzed To Predict User Locations. This Project Focuses On Predicting The Geographical Location Of Twitter Users Using Machine Learning Techniques Based On Textual And Metadata Features. The Proposed System Utilizes Natural Language Processing (NLP) Techniques To Preprocess Tweet Data, Including Tokenization, Stop-word Removal, And Feature Extraction Using Methods Such As TFIDF. Machine Learning Algorithms Such As Naïve Bayes, Support Vector Machine (SVM), Random Forest, And Logistic Regression Are Applied To Classify Tweets Into Different Geographic Locations. The Models Are Evaluated Using Performance Metrics Such As Accuracy, Precision, Recall, And F1-score. Experimental Results Show That Ensemble And Advanced Models Achieve Higher Accuracy Compared To Basic Classifiers. The System Provides A Scalable And Efficient Approach For Location Prediction, Which Can Be Useful In Applications Such As Disaster Management, Targeted Advertising, And Social Media Analytics.

Published:

08-4-2026

Issue:

Vol. 26 No. 4 (2026)

Page Nos:

1866-1871

Section:

Articles

License:

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

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