Sentiment Analysis Optimization  Using Ensemble of Multiple SVM Kernel Functions

M. Khairul Anam; Tri Putri Lestari; Lusiana Efrizoni; Nadya Satya Handayani; Imam Andhika

doi:10.29207/resti.v9i4.6708

Authors

M. Khairul Anam Universitas Samudra
Tri Putri Lestari Universitas Indraprasta PGRI
Lusiana Efrizoni Universitas Sains dan Teknologi Indonesia
Nadya Satya Handayani Politeknik Negeri Batam
Imam Andhika Universitas Syiah Kuala

DOI:

https://doi.org/10.29207/resti.v9i4.6708

Keywords:

Ensemble Learning, SMOTE, Kernel Function, Sentiment Analysis, Support Vector Machine

Abstract

This research aims to optimize sentiment analysis by leveraging the strengths of multiple Support Vector Machine (SVM) kernels—Linear, RBF, Polynomial, and Sigmoid—through an ensemble learning approach. This study introduces a novel model called SVM Porlis, which integrates these kernels using both hard and soft voting strategies to improve the classification performance on imbalanced datasets. Sentiment classification in this study involves two classes: positive and negative. Tweets related to the controversy over the naturalization of Indonesian national football players were collected using the official X/Twitter API, resulting in a dataset of 2,248 entries. The dataset was notably imbalanced, with significantly more negative samples than positive samples. Data preprocessing included cleaning, labeling, tokenization, stopword removal, stemming, and feature extraction using TF-IDF. To address the class imbalance, the SMOTE technique was applied to synthetically augment the minority class. Each SVM kernel was trained and evaluated individually before being combined into an SVM Porlis model. Evaluation metrics included accuracy, precision, recall, F1-score, and confusion matrix analysis. The results demonstrate that SVM Porlis with soft voting achieved the highest performance, with 98% accuracy, precision, recall, and F1-score, surpassing the performance of individual kernels and other ensemble approaches such as SVM + Chi-Square and SVM + PSO. These findings highlight the effectiveness of combining multiple kernels to capture both linear and non-linear patterns, offering a robust and adaptive solution for sentiment analysis in real-world, imbalanced data scenarios.

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