Arabic Emotion Recognition in Low-Resource Settings: A Novel Diverse Model Stacking Ensemble with Self-Training
Emotion recognition is a vital task within Natural Language Processing (NLP) that involves automatically identifying emotions from text. As the need for specialized and nuanced emotion recognition models increases, the challenge of fine-grained emotion recognition with limited labeled data becomes p...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/13/23/12772 |
| _version_ | 1797400483904618496 |
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| author | Maha Jarallah Althobaiti |
| author_facet | Maha Jarallah Althobaiti |
| author_sort | Maha Jarallah Althobaiti |
| collection | DOAJ |
| description | Emotion recognition is a vital task within Natural Language Processing (NLP) that involves automatically identifying emotions from text. As the need for specialized and nuanced emotion recognition models increases, the challenge of fine-grained emotion recognition with limited labeled data becomes prominent. Moreover, emotion recognition for some languages, such as Arabic, is a challenging task due to the limited availability of labeled data. This scarcity exists in both size and the granularity of emotions. Our research introduces a novel framework for low-resource fine-grained emotion recognition, which uses an iterative process that integrates a stacking ensemble of diverse base models and self-training. The base models employ different learning paradigms, including zero-shot classification, few-shot methods, machine learning algorithms, and transfer learning. Our proposed method eliminates the need for a large labeled dataset to initiate the training process by gradually generating labeled data through iterations. During our experiments, we evaluated the performance of each base model and our proposed method in low-resource scenarios. Our experimental findings indicate our approach outperforms the individual performance of each base model. It also outperforms the state-of-the-art Arabic emotion recognition models in the literature, achieving a weighted average F1-score equal to 83.19% and 72.12% when tested on the AETD and ArPanEmo benchmark datasets, respectively. |
| first_indexed | 2024-03-09T01:55:13Z |
| format | Article |
| id | doaj.art-d78d6fd39f8e4bee8d6ccd73e0c01c3a |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-09T01:55:13Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-d78d6fd39f8e4bee8d6ccd73e0c01c3a2023-12-08T15:11:40ZengMDPI AGApplied Sciences2076-34172023-11-0113231277210.3390/app132312772Arabic Emotion Recognition in Low-Resource Settings: A Novel Diverse Model Stacking Ensemble with Self-TrainingMaha Jarallah Althobaiti0Department of Computer Science, College of Computers and Information Technology, Taif University, Taif 21944, Saudi ArabiaEmotion recognition is a vital task within Natural Language Processing (NLP) that involves automatically identifying emotions from text. As the need for specialized and nuanced emotion recognition models increases, the challenge of fine-grained emotion recognition with limited labeled data becomes prominent. Moreover, emotion recognition for some languages, such as Arabic, is a challenging task due to the limited availability of labeled data. This scarcity exists in both size and the granularity of emotions. Our research introduces a novel framework for low-resource fine-grained emotion recognition, which uses an iterative process that integrates a stacking ensemble of diverse base models and self-training. The base models employ different learning paradigms, including zero-shot classification, few-shot methods, machine learning algorithms, and transfer learning. Our proposed method eliminates the need for a large labeled dataset to initiate the training process by gradually generating labeled data through iterations. During our experiments, we evaluated the performance of each base model and our proposed method in low-resource scenarios. Our experimental findings indicate our approach outperforms the individual performance of each base model. It also outperforms the state-of-the-art Arabic emotion recognition models in the literature, achieving a weighted average F1-score equal to 83.19% and 72.12% when tested on the AETD and ArPanEmo benchmark datasets, respectively.https://www.mdpi.com/2076-3417/13/23/12772fine-grained emotion recognitionlimited labeled dataArabic emotion recognitionstacking ensembleself-trainingfew-shot learning |
| spellingShingle | Maha Jarallah Althobaiti Arabic Emotion Recognition in Low-Resource Settings: A Novel Diverse Model Stacking Ensemble with Self-Training Applied Sciences fine-grained emotion recognition limited labeled data Arabic emotion recognition stacking ensemble self-training few-shot learning |
| title | Arabic Emotion Recognition in Low-Resource Settings: A Novel Diverse Model Stacking Ensemble with Self-Training |
| title_full | Arabic Emotion Recognition in Low-Resource Settings: A Novel Diverse Model Stacking Ensemble with Self-Training |
| title_fullStr | Arabic Emotion Recognition in Low-Resource Settings: A Novel Diverse Model Stacking Ensemble with Self-Training |
| title_full_unstemmed | Arabic Emotion Recognition in Low-Resource Settings: A Novel Diverse Model Stacking Ensemble with Self-Training |
| title_short | Arabic Emotion Recognition in Low-Resource Settings: A Novel Diverse Model Stacking Ensemble with Self-Training |
| title_sort | arabic emotion recognition in low resource settings a novel diverse model stacking ensemble with self training |
| topic | fine-grained emotion recognition limited labeled data Arabic emotion recognition stacking ensemble self-training few-shot learning |
| url | https://www.mdpi.com/2076-3417/13/23/12772 |
| work_keys_str_mv | AT mahajarallahalthobaiti arabicemotionrecognitioninlowresourcesettingsanoveldiversemodelstackingensemblewithselftraining |