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<p>A disability is a significant problem that has posed and proceeds to pose a challenge. Disability is frustrating because it is noted as a constraint, mental, physical, and cognitive handicap, which prevents the individual’s involvement and growth. Therefore, significant effort is brought into eliminating these types of restrictions. These plans deal with the problems that disabled people face. People with disabilities are frequently required to depend on others to fulfil their needs. Machine learning (ML) is outshining in making smart cities and providing a protected environment for disabled people. Emotional detection is an essential field of study that presents numerous recognized inputs. Emotion is phrased differently through facial and speech gestures, expressions, and written medium. Emotion detection in a text document is a content-based classification task using deep learning (DL) techniques, intricate methods, and natural language processing (NLP). This study proposes a Novel Hybrid Attention-Based Deep Learning for Textual Emotion Recognition Using Natural Language Processing Technologies (HADLTER-NLPT) technique. The HADLTER-NLPT technique aims to recognize emotions from textual data, improving assistive technologies and emotional understanding for disabled persons. Initially, the HADLTER-NLPT model performs text pre-processing at different levels to clean and normalize the input text. The Word2Vec model converts the textual data into dense vector representations that capture semantic meaning for the word embedding process. Furthermore, the hybrid attention-based long short-term memory (HA-LSTM) classifier effectively recognizes emotional expressions from text. The oscillating chaotic sunflower optimization (OCSFO) approach is employed for hyperparameter tuning to optimize the performance of the HA-LSTM approach. An extensive experimental study is performed on the HADLTER-NLPT method under Emotion detection from the text dataset. The performance validation of the HADLTER-NLPT method portrayed a superior accuracy value of 98.86% over existing models.</p>

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A novel hybrid attention based deep learning framework for textual emotion recognition using natural language processing technologies for disabled persons

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月28日

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  • Mohammed Abdullah Al-Hagery,
  • Abeer A. K. Alharbi,
  • Abdulwhab Alkharashi,
  • Ishfaq Yaseen

摘要

A disability is a significant problem that has posed and proceeds to pose a challenge. Disability is frustrating because it is noted as a constraint, mental, physical, and cognitive handicap, which prevents the individual’s involvement and growth. Therefore, significant effort is brought into eliminating these types of restrictions. These plans deal with the problems that disabled people face. People with disabilities are frequently required to depend on others to fulfil their needs. Machine learning (ML) is outshining in making smart cities and providing a protected environment for disabled people. Emotional detection is an essential field of study that presents numerous recognized inputs. Emotion is phrased differently through facial and speech gestures, expressions, and written medium. Emotion detection in a text document is a content-based classification task using deep learning (DL) techniques, intricate methods, and natural language processing (NLP). This study proposes a Novel Hybrid Attention-Based Deep Learning for Textual Emotion Recognition Using Natural Language Processing Technologies (HADLTER-NLPT) technique. The HADLTER-NLPT technique aims to recognize emotions from textual data, improving assistive technologies and emotional understanding for disabled persons. Initially, the HADLTER-NLPT model performs text pre-processing at different levels to clean and normalize the input text. The Word2Vec model converts the textual data into dense vector representations that capture semantic meaning for the word embedding process. Furthermore, the hybrid attention-based long short-term memory (HA-LSTM) classifier effectively recognizes emotional expressions from text. The oscillating chaotic sunflower optimization (OCSFO) approach is employed for hyperparameter tuning to optimize the performance of the HA-LSTM approach. An extensive experimental study is performed on the HADLTER-NLPT method under Emotion detection from the text dataset. The performance validation of the HADLTER-NLPT method portrayed a superior accuracy value of 98.86% over existing models.