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<p>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.</p>

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月21日

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  • Turki Ali Alghamdi,
  • Saud S. Alotaibi,
  • Reem Alharthi

摘要

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.