High Performance of Polynomial Kernel at SVM Algorithm for Sentiment Analysis

Author

Lailil Muflikhah, Dimas Joko Haryanto

Abstract

Sentiment analysis is a text mining based on the opinion collection towards the review of online product. Support Vector Machine (SVM) is an algorithm of classification that applicable to review the analysis of product. The hyperplane kernel function of SVM has importance role to classify the certain category. Therefore, this research is address to investigate the performance between Polynomial and Radial Basis Function (RBF) kernel functions for sentiment analysis of review product. They are examined to 200 comments using 10-fold validation and various parameter values (learning rate, lambda, c value, epsilon and iteration). As general, the performance for polynomial kernel of 88.75% is slightly higher than RBF kernel of 83.25%.

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References


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DOI: http://dx.doi.org/10.25126/jitecs.20183260