Performance of Machine Learning Algorithms on Imbalanced Sentiment Datasets Without Balancing Techniques

Performance of Machine Learning Algorithms on Imbalanced Sentiment Datasets Without Balancing Techniques

This study explores the performance of five sentiment classification algorithms—Naïve Bayes, Logistic Regression, Support Vector Machine, Decision Tree, and Random Forest—on an imbalanced sentiment dataset, with the SMOTE technique applied as a comparison. The research follows the Knowledge Discover...

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Bibliographic Details
Main Authors: Dina Wulan Yekti rahayu, Khothibul Umam, Maya Rini Handayani
Format: Article
Language:English
Published: Politeknik Negeri Batam 2025-06-01
Series:Journal of Applied Informatics and Computing
Subjects:
data mining
imbalanced datasets
sentiment analysis
Online Access:https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9584
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