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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| Format: | Article |
| Language: | English |
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Politeknik Negeri Batam
2025-06-01
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| Series: | Journal of Applied Informatics and Computing |
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| Online Access: | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9584 |
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| author | Dina Wulan Yekti rahayu Khothibul Umam Maya Rini Handayani |
| author_facet | Dina Wulan Yekti rahayu Khothibul Umam Maya Rini Handayani |
| author_sort | Dina Wulan Yekti rahayu |
| collection | DOAJ |
| description | 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 Discovery in Databases (KDD) framework, which includes data selection, preprocessing, transformation, data mining, and evaluation. The evaluation uses metrics such as accuracy, precision, recall, F1-score, and macro average F1-score. Initial results show that all five algorithms performed fairly well even without using a balancing technique, with Naïve Bayes achieving the highest F1-score of 0.84 and recall of 0.81. After applying SMOTE, only small improvements were observed in some models, such as Random Forest (F1-score increased from 0.81 to 0.85), while other models like Naïve Bayes experienced a decrease in performance, dropping to 0.77. This suggests that the effect of balancing techniques like SMOTE can vary depending on the algorithm. Thus, this study provides empirical contributions that highlight the importance of selecting appropriate approaches and the need for a deep understanding of each algorithm's behavior in the context of imbalanced data. Researchers are encouraged to carefully consider these aspects when designing experiments and interpreting results. |
| format | Article |
| id | doaj-art-8e9b1879f44946e294cc9df755cb8cfa |
| institution | DOAJ |
| issn | 2548-6861 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Politeknik Negeri Batam |
| record_format | Article |
| series | Journal of Applied Informatics and Computing |
| spelling | doaj-art-8e9b1879f44946e294cc9df755cb8cfa2025-08-20T02:45:02ZengPoliteknik Negeri BatamJournal of Applied Informatics and Computing2548-68612025-06-0193998100510.30871/jaic.v9i3.95847129Performance of Machine Learning Algorithms on Imbalanced Sentiment Datasets Without Balancing TechniquesDina Wulan Yekti rahayuKhothibul UmamMaya Rini HandayaniThis 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 Discovery in Databases (KDD) framework, which includes data selection, preprocessing, transformation, data mining, and evaluation. The evaluation uses metrics such as accuracy, precision, recall, F1-score, and macro average F1-score. Initial results show that all five algorithms performed fairly well even without using a balancing technique, with Naïve Bayes achieving the highest F1-score of 0.84 and recall of 0.81. After applying SMOTE, only small improvements were observed in some models, such as Random Forest (F1-score increased from 0.81 to 0.85), while other models like Naïve Bayes experienced a decrease in performance, dropping to 0.77. This suggests that the effect of balancing techniques like SMOTE can vary depending on the algorithm. Thus, this study provides empirical contributions that highlight the importance of selecting appropriate approaches and the need for a deep understanding of each algorithm's behavior in the context of imbalanced data. Researchers are encouraged to carefully consider these aspects when designing experiments and interpreting results.https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9584data miningimbalanced datasetssentiment analysis |
| spellingShingle | Dina Wulan Yekti rahayu Khothibul Umam Maya Rini Handayani Performance of Machine Learning Algorithms on Imbalanced Sentiment Datasets Without Balancing Techniques Journal of Applied Informatics and Computing data mining imbalanced datasets sentiment analysis |
| title | Performance of Machine Learning Algorithms on Imbalanced Sentiment Datasets Without Balancing Techniques |
| title_full | Performance of Machine Learning Algorithms on Imbalanced Sentiment Datasets Without Balancing Techniques |
| title_fullStr | Performance of Machine Learning Algorithms on Imbalanced Sentiment Datasets Without Balancing Techniques |
| title_full_unstemmed | Performance of Machine Learning Algorithms on Imbalanced Sentiment Datasets Without Balancing Techniques |
| title_short | Performance of Machine Learning Algorithms on Imbalanced Sentiment Datasets Without Balancing Techniques |
| title_sort | performance of machine learning algorithms on imbalanced sentiment datasets without balancing techniques |
| topic | data mining imbalanced datasets sentiment analysis |
| url | https://jurnal.polibatam.ac.id/index.php/JAIC/article/view/9584 |
| work_keys_str_mv | AT dinawulanyektirahayu performanceofmachinelearningalgorithmsonimbalancedsentimentdatasetswithoutbalancingtechniques AT khothibulumam performanceofmachinelearningalgorithmsonimbalancedsentimentdatasetswithoutbalancingtechniques AT mayarinihandayani performanceofmachinelearningalgorithmsonimbalancedsentimentdatasetswithoutbalancingtechniques |