Mhamed, Mustafa and Sutcliffe, Richard and Sun, Xia and Feng, Jun and Retta, Ephrem Afele (2023) Arabic sentiment analysis using GCL-based architectures and a customized regularization function. Engineering Science and Technology, an International Journal, 43. p. 101433. DOI https://doi.org/10.1016/j.jestch.2023.101433
Mhamed, Mustafa and Sutcliffe, Richard and Sun, Xia and Feng, Jun and Retta, Ephrem Afele (2023) Arabic sentiment analysis using GCL-based architectures and a customized regularization function. Engineering Science and Technology, an International Journal, 43. p. 101433. DOI https://doi.org/10.1016/j.jestch.2023.101433
Mhamed, Mustafa and Sutcliffe, Richard and Sun, Xia and Feng, Jun and Retta, Ephrem Afele (2023) Arabic sentiment analysis using GCL-based architectures and a customized regularization function. Engineering Science and Technology, an International Journal, 43. p. 101433. DOI https://doi.org/10.1016/j.jestch.2023.101433
Abstract
Sentiment analysis aims to extract emotions from textual data; with the proliferation of various social media platforms and the flow of data, particularly in the Arabic language, significant challenges have arisen, necessitating the development of various frameworks to handle issues. In this paper, we firstly design an architecture called Gated Convolution Long (GCL) to perform Arabic Sentiment Analysis. GCL can overcome difficulties with lengthy sequence training samples, extracting the optimal features that help improve Arabic sentiment analysis performance for binary and multiple classifications. The proposed method trains and tests in various Arabic datasets; The results are better than the baselines in all cases. GCL includes a Custom Regularization Function (CRF), which improves the performance and optimizes the validation loss. We carry out an ablation study and investigate the effect of removing CRF. CRF is shown to make a difference of up to 5.10% (2C) and 4.12% (3C). Furthermore, we study the relationship between Modern Standard Arabic and five Arabic dialects via a cross-dialect training study. Finally, we apply GCL through standard regularization (GCL+L1, GCL+L2, and GCL+LElasticNet) and our Lnew on two big Arabic sentiment datasets; GCL+Lnew gave the highest results (92.53%) with less performance time.
Item Type: | Article |
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Uncontrolled Keywords: | Arabic sentiment analysis (ASA); Custom regularization function (CRF); Gated convolution long (GCL); Natural language processing (NLP) |
Subjects: | Z Bibliography. Library Science. Information Resources > ZZ OA Fund (articles) |
Divisions: | Faculty of Science and Health Faculty of Science and Health > Computer Science and Electronic Engineering, School of |
SWORD Depositor: | Unnamed user with email elements@essex.ac.uk |
Depositing User: | Unnamed user with email elements@essex.ac.uk |
Date Deposited: | 20 Jul 2023 15:48 |
Last Modified: | 16 May 2024 21:53 |
URI: | http://repository.essex.ac.uk/id/eprint/36007 |
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