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2018 | OriginalPaper | Chapter

SVM Accuracy and Training Speed Trade-Off in Sentiment Analysis Tasks

Authors : Konstantinas Korovkinas, Paulius Danėnas, Gintautas Garšva

Published in: Information and Software Technologies

Publisher: Springer International Publishing

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Abstract

SVM technique is one of the best techniques to classify data, but it has a slow performance in the big data arrays. This paper introduces the method to improve the speed of SVM classification in sentiment analysis by reducing the training set. The method was tested on the Stanford Twitter sentiment corpus dataset and Amazon customer reviews dataset. The results show that the execution time of the introduced method outperforms the standard SVM classification method.

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Metadata
Title
SVM Accuracy and Training Speed Trade-Off in Sentiment Analysis Tasks
Authors
Konstantinas Korovkinas
Paulius Danėnas
Gintautas Garšva
Copyright Year
2018
Book
Information and Software Technologies

Print ISBN: 978-3-319-99971-5

Electronic ISBN: 978-3-319-99972-2

Copyright Year: 2018

DOI
https://doi.org/10.1007/978-3-319-99972-2_18

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