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November 21, 2025Scientific ReportsOpen Access

Textual emotion recognition to improve real-time communication of disabled people in sustainable environments using an ensemble deep learning approach

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Authors

TATurki Ali AlghamdiSASaud S. AlotaibiRAReem Alharthi

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Overview

This approach improves real-time communication by enhancing emotion detection in disabled individuals, indicating deep learning's role in text analysis.

Key Points

  • The aim is to enhance communication for disabled individuals by accurately recognizing emotions in text using a novel machine learning method.
  • Employs an ensemble deep learning approach for textual emotion recognition
  • Utilizes rigorous text pre-processing techniques to clean and normalize input data
  • Incorporates FastText for effective word embedding
  • Combines enhanced deep belief network and improved temporal convolutional network for emotion detection
  • Implements hyperparameter selection using sand cat swarm optimization to optimize model performance
  • Achieved an accuracy of 95.84% in detecting emotions from text
  • Demonstrated superiority over existing emotion detection models
  • Improved user experience for disabled individuals in real-time communication scenarios

Cite This Study

Alghamdi et al. (2025) studied this question.

synapsesocial.com/papers/6924e405c0ce034ddc34f781https://doi.org/10.1038/s41598-025-25363-z
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1An intelligent emotion recognition system for people with disabilities using stacked supervised autoencoder and brown-bear optimization strategy2026
  2. 2Emotion Analysis Using Improved Cat Swarm Optimization with Machine Learning for Speech-impaired People2024 · 2 citations
  3. 3Enhancing Textual Emotion Recognition to Aid People with Disabilities Using Brown-Bear Optimization with Stacked Supervised Autoencoder Model2025
  4. 4Enhancing Textual Emotion Recognition to Aid People with Disabilities Using Brown-Bear Optimization with Stacked Supervised Autoencoder Model2025
  5. 5Integration of corpus linguistics and deep learning techniques for enhanced semantic-driven emotion detection on textual data2025