Sentiment Analysis Optimization Using Ensemble of Multiple SVM Kernel Functions

Sentiment Analysis Optimization Using Ensemble of Multiple SVM Kernel Functions

ensemble learning; kernel function; sentiment analysis; smote; support vector machine

This study targets improved sentiment classification by combining the strengths of multiple SVM kernels within an ensemble framework. We introduce SVM Porlis, which fuses Linear, RBF, Polynomial, and Sigmoid kernels using both hard and soft voting to boost performance on skewed data. The task is binary sentiment recognition (positive vs. negative). A corpus of 2,248 tweets concerning the debate over the naturalization of Indonesia’s national football players was gathered via the official X/Twitter API, with a marked dominance of negative tweets. The preprocessing pipeline encompassed cleaning, labeling, tokenization, stopword removal, stemming, and TF-IDF feature extraction. To counter the imbalance, SMOTE was applied to synthesize additional minority-class samples. Each kernel was first trained and assessed independently, then aggregated into the SVM Porlis ensemble. Evaluation used accuracy, precision, recall, F1-score, and confusion-matrix analysis. The soft-voting SVM Porlis model achieved the best results—98% for accuracy, precision, recall, and F1—outperforming single-kernel baselines and otherensembles such as SVM + Chi-Square and SVM + PSO. These outcomes indicate that integrating diverse kernels effectively captures both linear and nonlinear patterns, yielding a robust and adaptive approach for sentiment analysis on real-world, imbalanced datasets

M. Khairul Anam1*,Tri Putri Lestari2, Lusiana Efrizoni3, Nadya Satya Handayani4, Imam Andhika

https://jurnal.iaii.or.id/index.php/RESTI/article/view/6708/1123

Departmentof Informatics, Facultyof Science and Technology, Universitas Samudra, Langsa, Indonesia

August 22, 2025

FAJAR BAGUS W

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