Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articles
Text Classification is the traditional Natural Language Processing (NLP) task. Text classification (also known as categorization) has become a cutting-edge research area in recent years. However, this task has received less attention in Arabic due to the need for more extensive resources for trainin...
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| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2025-01-01
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| Series: | Ain Shams Engineering Journal |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2090447924005707 |
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| author | Muhammad Swaileh A. Alzaidi Alya Alshammari Abdulkhaleq QA Hassan Shouki A. Ebad Hanan Al Sultan Mohammed A. Alliheedi Ali Abdulaziz Aljubailan Khadija Abdullah Alzahrani |
| author_facet | Muhammad Swaileh A. Alzaidi Alya Alshammari Abdulkhaleq QA Hassan Shouki A. Ebad Hanan Al Sultan Mohammed A. Alliheedi Ali Abdulaziz Aljubailan Khadija Abdullah Alzahrani |
| author_sort | Muhammad Swaileh A. Alzaidi |
| collection | DOAJ |
| description | Text Classification is the traditional Natural Language Processing (NLP) task. Text classification (also known as categorization) has become a cutting-edge research area in recent years. However, this task has received less attention in Arabic due to the need for more extensive resources for training Arabic text classifiers. In the area of text classification for Arabic news articles, deep learning (DL) methods, namely recurrent neural network (RNN) and convolutional neural network (CNN), were effectively used. This model is trained on labelled datasets around many news topics to automatically categorize articles into predetermined classes. These DL techniques can efficiently discern the subject matter by leveraging the contextual and semantic data embedded in the Arabic text, enabling accurate classification. This application of DL facilitates effective retrieval and organization of Arabic news articles, which supports tasks such as personalized content recommendations, information retrieval, and summarization. Therefore, this study presents an Enhanced Automated Text Categorization via Aquila Optimizer with Deep Learning for Arabic News Articles (TCAODL-ANA) technique. The TCAODL-ANA technique aims to detect and classify Arabic news articles into seven classes. The TCAODL-ANA technique follows pre-processing and the FastText word embedding process to accomplish this. In addition, the TCAODL-ANA technique utilizes an effective attention-based bidirectional gated recurrent unit (ABiGRU) method to identify various news articles. To enhance the detection results of the ABiGRU method, the AO model is employed for the hyperparameter selection process. A comprehensive simulation evaluation is performed to emphasize the improved performance of the TCAODL-ANA technique. The investigational validation portrayed the superior outcomes of the TCAODL-ANA technique over existing techniques. |
| format | Article |
| id | doaj-art-b5442c4da67948a79cf658b06c6ca489 |
| institution | Kabale University |
| issn | 2090-4479 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Ain Shams Engineering Journal |
| spelling | doaj-art-b5442c4da67948a79cf658b06c6ca4892025-01-17T04:49:20ZengElsevierAin Shams Engineering Journal2090-44792025-01-01161103189Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articlesMuhammad Swaileh A. Alzaidi0Alya Alshammari1Abdulkhaleq QA Hassan2Shouki A. Ebad3Hanan Al Sultan4Mohammed A. Alliheedi5Ali Abdulaziz Aljubailan6Khadija Abdullah Alzahrani7Department of English Language, College of Language Sciences, King Saud University, P. O. Box 145111, Riyadh, Saudi ArabiaDepartment of Applied Linguistics, College of Languages, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh 11671, Saudi ArabiaDepartment of English, College of Science and Arts at Mahayil, King Khalid University, Saudi ArabiaDepartment of Computer Science, Faculty of Science, Northern Border University, Arar 91431, Saudi Arabia; Corresponding author.Department of English, College of Arts, King Faisal University, Saudi ArabiaDepartment of Computing, Faculty of Computing and Information, Al-Baha University, Saudi ArabiaInstitute of Teaching Arabic for Non-Native Speakers, Imam Muhammad bin Saud Islamic University, Riyadh 11432, Saudi ArabiaSaudi Arabia Ministry of Education, Saudi ArabiaText Classification is the traditional Natural Language Processing (NLP) task. Text classification (also known as categorization) has become a cutting-edge research area in recent years. However, this task has received less attention in Arabic due to the need for more extensive resources for training Arabic text classifiers. In the area of text classification for Arabic news articles, deep learning (DL) methods, namely recurrent neural network (RNN) and convolutional neural network (CNN), were effectively used. This model is trained on labelled datasets around many news topics to automatically categorize articles into predetermined classes. These DL techniques can efficiently discern the subject matter by leveraging the contextual and semantic data embedded in the Arabic text, enabling accurate classification. This application of DL facilitates effective retrieval and organization of Arabic news articles, which supports tasks such as personalized content recommendations, information retrieval, and summarization. Therefore, this study presents an Enhanced Automated Text Categorization via Aquila Optimizer with Deep Learning for Arabic News Articles (TCAODL-ANA) technique. The TCAODL-ANA technique aims to detect and classify Arabic news articles into seven classes. The TCAODL-ANA technique follows pre-processing and the FastText word embedding process to accomplish this. In addition, the TCAODL-ANA technique utilizes an effective attention-based bidirectional gated recurrent unit (ABiGRU) method to identify various news articles. To enhance the detection results of the ABiGRU method, the AO model is employed for the hyperparameter selection process. A comprehensive simulation evaluation is performed to emphasize the improved performance of the TCAODL-ANA technique. The investigational validation portrayed the superior outcomes of the TCAODL-ANA technique over existing techniques.http://www.sciencedirect.com/science/article/pii/S2090447924005707Text CategorizationNatural Language ProcessingSentiment AnalysisAquila OptimizerDeep Learning |
| spellingShingle | Muhammad Swaileh A. Alzaidi Alya Alshammari Abdulkhaleq QA Hassan Shouki A. Ebad Hanan Al Sultan Mohammed A. Alliheedi Ali Abdulaziz Aljubailan Khadija Abdullah Alzahrani Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articles Ain Shams Engineering Journal Text Categorization Natural Language Processing Sentiment Analysis Aquila Optimizer Deep Learning |
| title | Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articles |
| title_full | Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articles |
| title_fullStr | Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articles |
| title_full_unstemmed | Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articles |
| title_short | Enhanced automated text categorization via Aquila optimizer with deep learning for Arabic news articles |
| title_sort | enhanced automated text categorization via aquila optimizer with deep learning for arabic news articles |
| topic | Text Categorization Natural Language Processing Sentiment Analysis Aquila Optimizer Deep Learning |
| url | http://www.sciencedirect.com/science/article/pii/S2090447924005707 |
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