Multitasking learning model based on hierarchical attention network for Arabic sentiment analysis classification
Limited approaches have been applied to Arabic sentiment analysis for a five-point classification problem. These approaches are based on single task learning with a handcrafted feature, which does not provide robust sentence representation. Recently, hierarchical attention networks have performed ou...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Published: |
MDPI
2021
|
| Online Access: | http://psasir.upm.edu.my/id/eprint/94301/ |
| _version_ | 1848861960376942592 |
|---|---|
| author | Alali, Muath Mohd Sharef, Nurfadhlina Azmi Murad, Masrah Azrifah Hamdan, Hazlina Husin, Nor Azura |
| author_facet | Alali, Muath Mohd Sharef, Nurfadhlina Azmi Murad, Masrah Azrifah Hamdan, Hazlina Husin, Nor Azura |
| author_sort | Alali, Muath |
| building | UPM Institutional Repository |
| collection | Online Access |
| description | Limited approaches have been applied to Arabic sentiment analysis for a five-point classification problem. These approaches are based on single task learning with a handcrafted feature, which does not provide robust sentence representation. Recently, hierarchical attention networks have performed outstandingly well. However, when training such models as single-task learning, these models do not exhibit superior performance and robust latent feature representation in the case of a small amount of data, specifically on the Arabic language, which is considered a low-resource language. Moreover, these models are based on single task learning and do not consider the related tasks, such as ternary and binary tasks (cross-task transfer). Centered on these shortcomings, we regard five ternary tasks as relative. We propose a multitask learning model based on hierarchical attention network (MTLHAN) to learn the best sentence representation and model generalization, with shared word encoder and attention network across both tasks, by training three-polarity and five-polarity Arabic sentiment analysis tasks alternately and jointly. Experimental results showed outstanding performance of the proposed model, with high accuracy of 83.98%, 87.68%, and 84.59 on LABR, HARD, and BRAD datasets, respectively, and a minimum macro mean absolute error of 0.632% on the Arabic tweets dataset for five-point Arabic sentiment classification problem. |
| first_indexed | 2025-11-15T13:09:26Z |
| format | Article |
| id | upm-94301 |
| institution | Universiti Putra Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-15T13:09:26Z |
| publishDate | 2021 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | upm-943012023-05-08T02:50:18Z http://psasir.upm.edu.my/id/eprint/94301/ Multitasking learning model based on hierarchical attention network for Arabic sentiment analysis classification Alali, Muath Mohd Sharef, Nurfadhlina Azmi Murad, Masrah Azrifah Hamdan, Hazlina Husin, Nor Azura Limited approaches have been applied to Arabic sentiment analysis for a five-point classification problem. These approaches are based on single task learning with a handcrafted feature, which does not provide robust sentence representation. Recently, hierarchical attention networks have performed outstandingly well. However, when training such models as single-task learning, these models do not exhibit superior performance and robust latent feature representation in the case of a small amount of data, specifically on the Arabic language, which is considered a low-resource language. Moreover, these models are based on single task learning and do not consider the related tasks, such as ternary and binary tasks (cross-task transfer). Centered on these shortcomings, we regard five ternary tasks as relative. We propose a multitask learning model based on hierarchical attention network (MTLHAN) to learn the best sentence representation and model generalization, with shared word encoder and attention network across both tasks, by training three-polarity and five-polarity Arabic sentiment analysis tasks alternately and jointly. Experimental results showed outstanding performance of the proposed model, with high accuracy of 83.98%, 87.68%, and 84.59 on LABR, HARD, and BRAD datasets, respectively, and a minimum macro mean absolute error of 0.632% on the Arabic tweets dataset for five-point Arabic sentiment classification problem. MDPI 2021 Article PeerReviewed Alali, Muath and Mohd Sharef, Nurfadhlina and Azmi Murad, Masrah Azrifah and Hamdan, Hazlina and Husin, Nor Azura (2021) Multitasking learning model based on hierarchical attention network for Arabic sentiment analysis classification. Electronics, 11 (8). art. no. 1193. pp. 1-23. ISSN 2079-9292 https://www.mdpi.com/2079-9292/11/8/1193 10.3390/electronics11081193 |
| spellingShingle | Alali, Muath Mohd Sharef, Nurfadhlina Azmi Murad, Masrah Azrifah Hamdan, Hazlina Husin, Nor Azura Multitasking learning model based on hierarchical attention network for Arabic sentiment analysis classification |
| title | Multitasking learning model based on hierarchical attention network for Arabic sentiment analysis classification |
| title_full | Multitasking learning model based on hierarchical attention network for Arabic sentiment analysis classification |
| title_fullStr | Multitasking learning model based on hierarchical attention network for Arabic sentiment analysis classification |
| title_full_unstemmed | Multitasking learning model based on hierarchical attention network for Arabic sentiment analysis classification |
| title_short | Multitasking learning model based on hierarchical attention network for Arabic sentiment analysis classification |
| title_sort | multitasking learning model based on hierarchical attention network for arabic sentiment analysis classification |
| url | http://psasir.upm.edu.my/id/eprint/94301/ http://psasir.upm.edu.my/id/eprint/94301/ http://psasir.upm.edu.my/id/eprint/94301/ |