High Performance of Polynomial Kernel at SVM Algorithm for Sentiment Analysis | Zendy

Lailil Muflikhah | Zendy; Dimas Joko Haryanto | Zendy

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High Performance of Polynomial Kernel at SVM Algorithm for Sentiment Analysis

Author(s) -

Lailil Muflikhah,

Dimas Joko Haryanto

Publication year - 2018

Publication title -

journal of information technology and computer science

Language(s) - English

Resource type - Journals

eISSN - 2540-9824

pISSN - 2540-9433

DOI - 10.25126/jitecs.20183260

Subject(s) - polynomial kernel , radial basis function kernel , support vector machine , kernel (algebra) , radial basis function , product (mathematics) , hyperplane , polynomial , artificial intelligence , computer science , sentiment analysis , function (biology) , mathematics , kernel method , pattern recognition (psychology) , algorithm , machine learning , combinatorics , artificial neural network , mathematical analysis , geometry , evolutionary biology , biology

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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