Comparison of the accuracy of SVM kemel functions in text classification

Neli Kalcheva, Milena Karova, Ivaylo Penev

202036 citationsDOI

Abstract

The objective of this paper is to compare the accuracy of different kernel functions of the SVM method for text classification. As a basis for the research film reviews are used. The authors try to detect the kernel functions and their parameters to achieve high accuracy in movie reviews classification. The studied kernel functions are: polynomial kernel of degree 2, a linear kernel and a radial base kernel. The achieved accuracy is higher than 83%. The experiments show that the sigmoid radial kernel is an inappropriate choice in text classification.

Topics & Concepts

Polynomial kernelRadial basis function kernelKernel (algebra)Support vector machinePattern recognition (psychology)Artificial intelligenceSigmoid functionKernel methodTree kernelComputer scienceRadial basis functionKernel embedding of distributionsVariable kernel density estimationPolynomialMathematicsDiscrete mathematicsArtificial neural networkMathematical analysisSentiment Analysis and Opinion MiningText and Document Classification TechnologiesSpam and Phishing Detection