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Empowering people with intellectual disabilities using integrated deep learning architecture driven enhanced

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This study introduces a novel hybrid deep learning model for intelligent emotion recognition from text. The proposed method significantly improves accuracy in detecting emotions within textual data, aiding communication for individuals with disabilities.

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Area of Science:

  • Artificial Intelligence
  • Natural Language Processing
  • Computational Linguistics

Background:

  • Emotion recognition is crucial in psychology, healthcare, and human-computer interaction (HCI).
  • Conventional methods for textual emotion recognition (TER) often rely on limited data and have reliability issues.
  • Deep learning (DL) advancements have spurred significant progress in TER.

Purpose of the Study:

  • To develop an advanced deep learning system for accurate text emotion recognition.
  • To enhance communication accessibility for people with disabilities through improved TER.
  • To introduce the Intelligent Emotion Recognition from Text Using a Hybrid Deep Learning Model and Word Embedding Process (IERT-HDLMWEP) model.

Main Methods:

  • Text pre-processing to reduce dimensionality and prepare data for analysis.
  • Hybrid feature representation integrating Word2Vec, TF-IDF weighting, and Part-of-Speech features.
  • Classification using a hybrid Convolutional Neural Network (CNN) and Bidirectional Gated Recurrent Unit (BiGRU) with an attention mechanism (C-BiG-A).

Main Results:

  • The IERT-HDLMWEP model demonstrated superior performance in emotion detection from text datasets.
  • Empirical results confirmed the effectiveness of the proposed hybrid deep learning approach.
  • The methodology showed significant improvements over existing TER techniques.

Conclusions:

  • The IERT-HDLMWEP model offers a robust and accurate solution for textual emotion recognition.
  • This approach has the potential to significantly aid individuals with communication disabilities.
  • The study highlights the efficacy of hybrid deep learning architectures in advancing NLP tasks.