Textual emotion recognition to improve real-time communication of disabled people in sustainable environments using an ensemble deep learning approach Article Swipe
Turki Ali Alghamdi
,
Saud S. Alotaibi
,
Reem Alharthi
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-25363-z
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1038/s41598-025-25363-z
Social media platforms are prevalently used to express and share opinions on a wide range of topics, which has amplified interest in textual emotion detection. However, accurately detecting emotions in individuals, especially those with communication challenges, remains a complex task. Emotion analysis serves as a significant tool for assessing, monitoring, and interpreting a user’s sentiments toward services or products. The emergence of deep learning (DL) has significantly advanced this field, allowing the development of more accurate and robust models. DL techniques, particularly neural networks, have demonstrated superior performance in recognizing emotions from text, presenting enhanced capabilities for real-time sentiment understanding and user experience improvement. This manuscript presents an Optimised Ensemble Model for Precise Textual Emotion Recognition Using an Improved Sand Cat Swarm Optimization (OEMPTER-ISCSO) method. The primary objective of the OEMPTER-ISCSO method is to accurately recognize emotions in text, facilitating enhanced communication with individuals with disabilities. Initially, the text pre-processing stage involves multiple levels to normalize and clean the input text. Furthermore, the FastText method is employed for the word embedding process, transforming words into numerical vector representations. For textual emotion detection, an ensemble of three classifiers, such as the enhanced deep belief network (EDBN), Elman neural network (ELNN), and an improved temporal convolutional network (ITCN) method, is employed. Finally, the enhanced sand cat swarm optimization (ISCO) method-based hyperparameter selection procedure is executed to optimize the detection outcomes of the ensemble models. The OEMPTER-ISCSO technique achieved a superior accuracy of 95.84% in a comparative analysis on a text-based emotion detection dataset, demonstrating its efficiency over existing models.
Related Topics
Concepts
Computer science
Artificial intelligence
Hyperparameter
Deep learning
Convolutional neural network
Sentiment analysis
Emotion recognition
Emotion classification
Ensemble forecasting
Machine learning
Ensemble learning
Word embedding
Deep belief network
Emotion detection
Word (group theory)
Selection (genetic algorithm)
Embedding
Artificial neural network
Recurrent neural network
Range (aeronautics)
Deep neural networks
Pattern recognition (psychology)
Ground truth
Natural language processing
Support vector machine
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1038/s41598-025-25363-z
- https://www.nature.com/articles/s41598-025-25363-z.pdf
- OA Status
- gold
- References
- 43
- OpenAlex ID
- https://openalex.org/W7106338849
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7106338849Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1038/s41598-025-25363-zDigital Object Identifier
- Title
-
Textual emotion recognition to improve real-time communication of disabled people in sustainable environments using an ensemble deep learning approachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-21Full publication date if available
- Authors
-
Turki Ali Alghamdi, Saud S. Alotaibi, Reem AlharthiList of authors in order
- Landing page
-
https://doi.org/10.1038/s41598-025-25363-zPublisher landing page
- PDF URL
-
https://www.nature.com/articles/s41598-025-25363-z.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
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https://www.nature.com/articles/s41598-025-25363-z.pdfDirect OA link when available
- Concepts
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Computer science, Artificial intelligence, Hyperparameter, Deep learning, Convolutional neural network, Sentiment analysis, Emotion recognition, Emotion classification, Ensemble forecasting, Machine learning, Ensemble learning, Word embedding, Deep belief network, Emotion detection, Word (group theory), Selection (genetic algorithm), Embedding, Artificial neural network, Recurrent neural network, Range (aeronautics), Deep neural networks, Pattern recognition (psychology), Ground truth, Natural language processing, Support vector machineTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- References (count)
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43Number of works referenced by this work
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| abstract_inverted_index.(ELNN), | 199 |
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