Article Per Year (5 Year)
p-Index From 2021 - 2026
0.23
P-Index
This Author published in this journals
All Journal IAES International Journal of Artificial Intelligence (IJ-AI)
Tabarek Alwan Tuib
University of Tabriz
Computer Science & IT Engineering

Published : 1 Documents Claim Missing Document
Claim Missing Document
Check
Articles

Found 1 Documents
Search

 1 
Text emotion recognition based on deep learning and attention with whale optimization algorithm Tabarek Alwan Tuib; Mohammad-Reza Feizi-Derakhshi; Yaqdhan Mahmood Hussein; Fahad Taha Al-Dhief
IAES International Journal of Artificial Intelligence (IJ-AI) Vol 15, No 4: August 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijai.v15.i4.pp3692-3702

Abstract

Text emotion recognition (TER) represents one of the crucial tasks in natural language processing (NLP), and it is highly important for many applications. To improve the performance of TER, advanced deep learning (DL) model GRU-BiGRU-CNN-Att is presented in this research. It is a combination of gated recurrent units (GRU), bidirectional gated recurrent units (BiGRU) with a convolutional neural network (CNN), and an attention mechanism. Using a GRU, BiGRU, and CNN as part of a deep feature extraction model, combined with an attention mechanism that gives important information different weights, the method that is proposed in the present work enhances the quality of the word vectors and leads to increasing sentiment analysis judgment accuracy. The whale optimization algorithm (WOA), which selects the most informative features for further enhancing the model, is used lastly for the optimization of the feature selection process. After such optimization, those chosen features are trained on a multi-layer perceptron (MLP), successfully combining machine learning (ML) and DL approaches for improving TER. A varied corpus of tweets, sentences, and dialogues had been used in the present work for the assessment of the performance of the suggested model. The proposed method achieves 83.76% accuracy in emotion recognition.
Co-Authors Fahad Taha Al-Dhief Mohammad-Reza Feizi-Derakhshi Yaqdhan Mahmood Hussein