000 06322nam a22002657a 4500
008 201210b2025 a|||f bm|| 00| 0 eng d
024 7 _a0000-0001-7713-9682
_2ORCID
040 _aEG-CaNU
_cEG-CaNU
041 0 _aeng
_beng
_bara
082 _a610
100 0 _aYomna Eid Mohamed Rizk
_93672
245 1 _aMulti-Domain Aspect-Based Sentiment Analysis Using Transformer-Based Models On Arabic Corpus
_c/Yomna Eid Mohamed Rizk
260 _c2025
300 _a p.
_bill.
_c21 cm.
500 _3Supervisor: Walaa Medhat
502 _aThesis (M.A.)—Nile University, Egypt, 2025 .
504 _a"Includes bibliographical references"
505 0 _aContents: Contents Page CERTIFICATION OF APPROVAL ii COPYRIGHT iii ACKNOWLEDGEMENTS iv ABSTRACT v Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . vi LIST OF FIGURES xi LIST OF TABLES xii LIST OF EQUATIONS xiii Chapters: 1. Introduction 1 1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 1.3 Thesis Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3 1.4 Organization of the Thesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4 2. Background and Concepts 5 2.1 Sentiment Analysis (SA) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5 2.2 Aspect-Based Sentiment Analysis (ABSA) Task . . . . . . . . . . . . . . . . . . . . . 6 2.2.1 ABSA in the Arabic Language . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 vii 2.2.2 Models for ABSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.3 Multi-domain ABSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13 3. Background and Literature Review 15 3.1 Existing ABSA Models in English . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 3.2 Arabic Single-Domain ABSA Datasets . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3.3 Arabic Single-Domain ABSA Models . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 3.4 Multi-domain ABSA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 3.4.1 Methodologies of Multi-Domain ABSA . . . . . . . . . . . . . . . . . . . . . . 23 3.4.2 Models for Multi-Domain ABSA . . . . . . . . . . . . . . . . . . . . . . . . . . 23 4. Multi-Domain Aspect-Based Sentiment Analysis Proposed Model 26 4.1 A-MASA: Arabic Multi-Domain Aspect-Based Sentiment Analysis Datasets . . . . . . 27 4.1.1 Dataset Construction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 4.1.2 Dataset Cleaning . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 4.1.3 Dataset Annotation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 4.1.4 Dataset Preparation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4.2 ABSA Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 5. Experimental Evaluation 40 5.1 Experimental Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 5.2 A-MASA Evaluation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 5.3 Muli-Domain ABSA Models Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 5.3.1 Muli-Domain Aspects Extraction Results . . . . . . . . . . . . . . . . . . . . . 45 5.3.2 Muli-Domain Aspects Sentiment Classification Results . . . . . . . . . . . . . 48 5.4 Other Models Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49 5.4.1 Comparison Between Our Model and Arabic Single-domain AE Model . . . . . 49 5.4.2 Comparison Between Multi-domain Arabic ABSA Model and LLMs-based Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50 6. Conclusions and Future Work 52 viii 6.1 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 6.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53 Bibliography 60 Appendices: A. Publications
520 3 _aAbstract: Aspect-Based Sentiment Analysis (ABSA) is a fine-grained Sentiment Analysis (SA) task that identifies sentiments expressed towards specific aspects within a sentence. Despite the increasing use of Semitic languages like Arabic in Natural Language Processing (NLP), there remains a scarcity of resources and datasets for Arabic ABSA. To address these challenges, we introduce a new manually annotated multi-domain dataset, A-MASA (Arabic Multi-domain Aspect-based Sentiment Analysis), comprising 6,500 records from different domains and dialects. Additionally, a generalized model for Arabic multi-domain ABSA was developed and trained on both A-MASA and existing datasets, covering a total of nine domains. The proposed model integrates three state-of-the-art pre-trained models, each fine-tuned with additional layers, and combines their outputs using a weighted sum ensemble technique. Comprehensive experiments were conducted to evaluate the model’s performance. For the Aspect Extraction (AE) task, the QARiB model achieved the highest F1-score of 88.7% on the hotels domain, while the ensemble model reached the highest F1-score of 88.9% on the products domain. For aspect sentiment classification, the ensemble model recorded an F1-score of 87.5% on the hotels domain. The ensemble model demonstrated superior generalization, outperforming existing single-domain models on unseen data, achieving 37% improvement in the books domain and 14% in the hotels domain for the AE task. Additionally, the model surpassed two Large Language Models (LLMs), achieving an F1-score of 88.9% in the products domain 49% higher than JAIS and 31% higher than Gemini. keywords Arabic Aspect-Based Sentiment Analysis (ABSA), Multi-domain Sentiment Analysis, Natural Language Processing (NLP), Ensemble Learning Models, Arabic Datasets.
546 _aText in English, abstracts in English and Arabic
650 4 _aInformaticsIFM
655 7 _2NULIB
_aDissertation, Academic
_9187
690 _aInformaticsIFM
942 _2ddc
_cTH
999 _c11014
_d11014