Title

Combining RSS-SVM with genetic algorithm for Arabic opinions analysis

Document Type

Article

Source of Publication

International Journal of Intelligent Systems Technologies and Applications

Publication Date

1-1-2019

Abstract

Copyright © 2019 Inderscience Enterprises Ltd. Due to the large-scale users of the Arabic language, researchers are drawn to the Arabic sentiment analysis and precisely the classification areas. Thus, the most accurate classification technique used in this area is the support vector machine (SVM) classifier. This last, is able to increase the rates in opinion mining but with use of very small number of features. Hence, reducing feature’s vector can alternate the system performance by deleting some pertinent ones. To overcome these two constraints, our idea is to use random sub space (RSS) algorithm to generate several features vectors with limited size; and to replace the decision tree base classifier of RSS with SVM. Later, another proposition was implemented in order to enhance the previous algorithm by using the genetic algorithm as subset features generator based on correlation criteria to eliminate the random choice used by RSS and to prevent the use of incoherent features subsets.

ISSN

1740-8865

Publisher

Inderscience Publishers

Volume

18

Issue

1-2

First Page

152

Last Page

178

Disciplines

Computer Sciences | Social and Behavioral Sciences

Keywords

Arabic opinion mining, GA, Genetic algorithm, Machine learning, Random sub space, RSS, SentiWordNet, Support vector machine, SVM

Scopus ID

85061301010

Indexed in Scopus

yes

Open Access

yes

Open Access Type

Bronze: This publication is openly available on the publisher’s website but without an open license

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