Performance Analysis of SVM Kernels in Sentiment Classification on Indonesian Local Skincare Dataset

Abstract

Purpose: Sentiment analysis is an important aspect of understanding consumers' views on products, especially in the growing skincare industry. This study aims to compare the accuracy and effectiveness of various kernels in the Support Vector Machine (SVM) algorithm, including linear, polynomial (poly), and radial basis function (RBF) kernels, in predicting three types of sentiment: positive, neutral, and negative based on reviews of local Indonesian skincare products.

Design/methodology/approach: The dataset used includes consumer reviews classified by rating, which are then processed using Term Frequency-Inverse Document Frequency (TF-IDF) technique for feature extraction.

Findings/result: The evaluation results show that the RBF kernel achieves the highest accuracy of 74.78%, followed by the linear kernel with 74.51% accuracy, and the polynomial kernel with 74.10% accuracy. Although the difference between the three kernels is not significant, the RBF kernel excels in positive sentiment classification, while all three kernels struggle in predicting neutral and negative classes.

Originality/value/state of the art: These findings make an important contribution to the development of effective sentiment analysis methods, especially in the context of datasets with high class imbalance. To handle class imbalance, techniques such as oversampling smaller classes or using cost-sensitive learning techniques to give more weight to negative and neutral classes can be used.