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Arabic Text Classification Using Convolutional Neural Network and Genetic Algorithms
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DOI：10.1109/ACCESS.2021.3091376.png)
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Abstract
En 中文
Arabic documents are massively rising due to numerous contents utilized in websites, social media, and news articles. The classification of such documents in labelled categories is a significant and vital task that deserves more attention. Arabic Text Classification is an emerging research theme in Arabic Natural Language Processing. Recently, Deep Neural Network approaches have successfully been applied to many text classification problems, especially in English Text Classification. Convolutional Neural Network (CNN) is one of the best popular models. However, CNN is not highly applied in Arabic Text Classification. In addition, the recent studies did not achieve a high classification accuracy due to parameter setting issue. To overcome this limitation, a new hybrid classification model for Arabic Text is developed. This paper proposes Genetic Algorithms based Convolutional Neural Network for Arabic Text Classification. Genetic Algorithm is used to optimize the CNN parameters. The proposed model is tested using two large datasets and compared with the state-of-the art studies. The results showed that the classification accuracy achieved an improvement of 4 to 5%.
Keywords:
Genetic algorithms
Convolutional neural networks
Support vector machines
Deep learning
Natural language processing
Feature extraction
Social networking (online)
Natural language processing
Arabic text classification
convolutional neural networks
genetic algorithm
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