Combining RSS-SVM with genetic algorithm for Arabic opinions analysis
| Autor: | Soraya Cheriguene, Djamel Zenakhra, Monther Aldwairi, Amel Ziani, Nabiha Azizi |
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| Rok vydání: | 2019 |
| Předmět: |
General Computer Science
Arabic business.industry Computer science RSS Small number Sentiment analysis Decision tree 020206 networking & telecommunications Pattern recognition 02 engineering and technology computer.file_format language.human_language Support vector machine Correlation 0202 electrical engineering electronic engineering information engineering language 020201 artificial intelligence & image processing Artificial intelligence business computer Classifier (UML) |
| Zdroj: | International Journal of Intelligent Systems Technologies and Applications. 18:152 |
| ISSN: | 1740-8873 1740-8865 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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