Combining RSS-SVM with genetic algorithm for Arabic opinions analysis
Source of Publication
International Journal of Intelligent Systems Technologies and Applications
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.
Computer Sciences | Social and Behavioral Sciences
Arabic opinion mining, GA, Genetic algorithm, Machine learning, Random sub space, RSS, SentiWordNet, Support vector machine, SVM
Ziani, Amel; Azizi, Nabiha; Zenakhra, Djamel; Cheriguene, Soraya; and Aldwairi, Monther, "Combining RSS-SVM with genetic algorithm for Arabic opinions analysis" (2019). All Works. 971.
Indexed in Scopus
Open Access Type
Bronze: This publication is openly available on the publisher’s website but without an open license