ENHANCING NATURAL LANGUAGE PROCESSING USING WALRUS OPTIMIZER WITH SELF-ATTENTION DEEP LEARNING MODEL IN APPLIED LINGUISTICS

Abdulkhaleq Q. A. Hassan, Alya Alshammari, Belal Zaqaibeh, Muhammad Swaileh A. Alzaidi, Randa Allafi, Sana Alazwari, Jawhara Aljabri and Amal M Nouri
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Abdulkhaleq Q. A. Hassan: Department of English, College of Science and Arts at Mahayil, King Khalid University, Saudi Arabia
Alya Alshammari: ��Department of Applied Linguistics, College of Languages, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Belal Zaqaibeh: ��Faculty of Science and Information Technology, Jordan University, Jordan
Muhammad Swaileh A. Alzaidi: �Department of English Language, College of Language Sciences, King Saud University, P. O. Box 145111, Riyadh, Saudi Arabia
Randa Allafi: �Department of Computers and Information Technology, College of Sciences and Arts, Northern Border University, Arar, Saudi Arabia
Sana Alazwari: ��Department of Information Technology, College of Computers and Information Technology, Taif University, P. O. Box 11099, Taif 21944, Saudi Arabia
Jawhara Aljabri: *Department of Computer Science, University College in Umluj, University of Tabuk, Saudi Arabia
Amal M Nouri: ��†Department of Computer Science, Applied College, Imam Abdulrahman Bin Faisal University, Dammam 34212, Saudi Arabia

FRACTALS (fractals), 2024, vol. 32, issue 09n10, 1-19

Abstract: Sentiment analysis (SA) is the popular natural language processing (NLP) problem utilized for analyzing texts, including uploaded reviews and user posts on e-commerce portals, forums, and social media platforms, regarding their opinions about the event or person, product, and service. The SA task comprises analyzing text to determine whether the sentiment expressed is negative, positive, or neutral, aiming for precise subjective data analysis. The deep learning (DL) technique enables various architectures to model SA tasks and has surpassed other machine learning (ML) techniques as the first method to perform SA tasks. Recent advancements in the DL model depart from the growing dominance of transformer language algorithms and convolutional neural network (CNN) and recurrent neural network (RNN) models. Utilizing pre-trained transformer language models to transfer knowledge to downstream tasks has emerged as a cutting-edge model in NLP. Therefore, the study designs a fractal walrus optimizer with self-attention DL-based SA in applied linguistics (WOSADL-SAAL) method. The WOSADL-SAAL method aims to recognize and classify the presence of sentiments in social media content. In the WOSADL-SAAL technique, data preprocessing is initially performed to transform the input dataset into a meaningful format. In addition, the WOSADL-SAAL technique uses the bag of words (BoW) model for extracting features. The self-attention bidirectional long short-term memory (SA-BiLSTM) network is applied to classify sentiment. The walrus optimizer (WO) model performs the hyperparameter tuning model to boost the detection outcomes of the SA-BiLSTM network. The performance evaluation of the WOSADL-SAAL method takes place under the benchmark SA dataset. The experimental analysis of the WOSADL-SAAL method exhibited a superior accuracy value of 99.07% and 99.24% under TUSA and IMDB datasets over existing approaches.

Keywords: Sentiment Analysis; Natural Language Processing; Fractal Walrus Optimizer; Deep Learning; Bag of Words (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1142/S0218348X25400067

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