Improving Arabic sentiment analysis across context-aware attention deep model based on natural language processing.
With the enormous growth of social data in recent years, sentiment analysis has gained increasing research attention and has been widely explored in various languages. Arabic language nature imposes several challenges, such as the complicated morphological structure and the limited resources, Thereb...
| Publicado en: | Language Resources & Evaluation Vol. 59; no. 2; pp. 639 - 664 |
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| Autores principales: | , , |
| Formato: | Artículo |
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Springer Nature
Jun2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=185240036&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 185240036 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 1574020X 179V jtl: Language Resources & Evaluation issn: 1574020X maglogo: N pubinfo: dt: Jun2025 vid: 59 iid: 2 pid: 237 pub: Springer Nature artinfo: ui: 185240036 10.1007/s10579-024-09741-z ppf: 639 ppct: 25 formats: fmt: – @attributes: type: T – @attributes: type: P size: 1.6MB tig: atl: Improving Arabic sentiment analysis across context-aware attention deep model based on natural language processing. aug: au: Ombabi, Abubakr H. Ouarda, Wael Alimi, Adel M. affil: Faculty of Computer Science and Information Technology, University of Al-Butana, Rufaa, Gezira, Sudan https://ror.org/02s48dm85 Digital Research Center of Sfax (CRNS), Sakiet Ezzit, Sfax, Tunisia https://ror.org/04d4sd432 REGIM Lab: Research Groups in Intelligent Machines, National Engineering School of Sfax, University of Sfax, Sfax, Tunisia https://ror.org/04z6c2n17 Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa su: Convolutional neural networks Natural language processing Long short-term memory Cognitive psychology Sentiment analysis sug: subj: Convolutional neural networks Natural language processing Long short-term memory Cognitive psychology Sentiment analysis keyword: Attention mechanism Deep learning Opinion mining Psychology and Cognitive Sciences Psychology Text classification ab: With the enormous growth of social data in recent years, sentiment analysis has gained increasing research attention and has been widely explored in various languages. Arabic language nature imposes several challenges, such as the complicated morphological structure and the limited resources, Thereby, the current state-of-the-art methods for sentiment analysis remain to be enhanced. This inspired us to explore the application of the emerging deep-learning architecture to Arabic text classification. In this paper, we present an ensemble model which integrates a convolutional neural network, bidirectional long short-term memory (Bi-LSTM), and attention mechanism, to predict the sentiment orientation of Arabic sentences. The convolutional layer is used for feature extraction from the higher-level sentence representations layer, the BiLSTM is integrated to further capture the contextual information from the produced set of features. Two attention mechanism units are incorporated to highlight the critical information from the contextual feature vectors produced by the Bi-LSTM hidden layers. The context-related vectors generated by the attention mechanism layers are then concatenated and passed into a classifier to predict the final label. To disentangle the influence of these components, the proposed model is validated as three variant architectures on a multi-domains corpus, as well as four benchmarks. Experimental results show that incorporating Bi-LSTM and attention mechanism improves the model's performance while yielding 96.08% in accuracy. Consequently, this architecture consistently outperforms the other State-of-The-Art approaches with up to + 14.47%, + 20.38%, and + 18.45% improvements in accuracy, precision, and recall respectively. These results demonstrated the strengths of this model in addressing the challenges of text classification tasks. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Language Resources & Evaluation is a copyright of Springer, 2025. All Rights Reserved. item: Language Resources & Evaluation holder: Springer Nature dt: @attributes: year: 2025 holdings: @attributes: islocal: N |
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