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© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.

Abstract

The task of analyzing sentiment has been extensively researched for a variety of languages. However, due to a dearth of readily available Natural Language Processing methods, Urdu sentiment analysis still necessitates additional study by academics. When it comes to text processing, Urdu has a lot to offer because of its rich morphological structure. The most difficult aspect is determining the optimal classifier. Several studies have incorporated ensemble learning into their methodology to boost performance by decreasing error rates and preventing overfitting. However, the baseline classifiers and the fusion procedure limit the performance of the ensemble approaches. This research made several contributions to incorporate the symmetries concept into the deep learning model and architecture: firstly, it presents a new meta-learning ensemble method for fusing basic machine learning and deep learning models utilizing two tiers of meta-classifiers for Urdu. The proposed ensemble technique combines the predictions of both the inter- and intra-committee classifiers on two separate levels. Secondly, a comparison is made between the performance of various committees of deep baseline classifiers and the performance of the suggested ensemble Model. Finally, the study’s findings are expanded upon by contrasting the proposed ensemble approach efficiency with that of other, more advanced ensemble techniques. Additionally, the proposed model reduces complexity, and overfitting in the training process. The results show that the classification accuracy of the baseline deep models is greatly enhanced by the proposed MLE approach.

Details

Title
Contextually Enriched Meta-Learning Ensemble Model for Urdu Sentiment Analysis
Author
Ahmed, Kanwal 1   VIAFID ORCID Logo  ; Nadeem, Muhammad Imran 1   VIAFID ORCID Logo  ; Li, Dun 1 ; Zheng, Zhiyun 1 ; Al-Kahtani, Nouf 2   VIAFID ORCID Logo  ; Hend Khalid Alkahtani 3   VIAFID ORCID Logo  ; Mostafa, Samih M 4   VIAFID ORCID Logo  ; Mamyrbayev, Orken 5   VIAFID ORCID Logo 

 School of Computer and Artificial Intelligence, Zhengzhou University, Zhengzhou 450001, China 
 Department of Health Information Management and Technology, College of Public Health, Imam Abdulrahman Bin Faisal University, Dammam 31441, Saudi Arabia 
 Department of Information Systems, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, Riyadh 11671, Saudi Arabia 
 Computer Science Department, Faculty of Computers and Information, South Valley University, Qena 83523, Egypt 
 Institute of Information and Computational Technologies, Almaty 050010, Kazakhstan 
First page
645
Publication year
2023
Publication date
2023
Publisher
MDPI AG
e-ISSN
20738994
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2791740230
Copyright
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.