APPLIED LINGUISTICS-DRIVEN ARTIFICIAL INTELLIGENCE APPROACH FOR SENTIMENT ANALYSIS AND CLASSIFICATION ON SOCIAL MEDIA

Asma A. Alhashmi, Alya Alshammari, Muhammad Kashif Saeed, Muhammad Swaileh A. Alzaidi, Fuhid Alanazi and Ahmed Sayed
Additional contact information
Asma A. Alhashmi: Department of Computer Science at College of Science, Northern Border University, Arar, Saudi Arabia
Alya Alshammari: ��Department of Applied Linguistics, College of Languages, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Muhammad Kashif Saeed: ��Department of Computer Science, Applied College at Mahayil, King Khalid University, Saudi Arabia
Muhammad Swaileh A. Alzaidi: �Department of English Language, College of Language Sciences, King Saud University, P. O. Box 145111, Riyadh, Saudi Arabia
Fuhid Alanazi: �Department of Information Systems, Faculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi Arabia
Ahmed Sayed: ��Research Center, Future University in Egypt, New Cairo 11835, Egypt

FRACTALS (fractals), 2025, vol. 33, issue 02, 1-16

Abstract: Sentiment analysis (SA) is an essential application of machine learning (ML) and natural language processing (NLP) that comprises the automatic extraction of opinions or sentiments presented in textual data. By leveraging methods to distinguish the expressive nature conveyed in written content, SA permits businesses and research workers to gain valuable insights into social media discourse, customer feedback, and public reviews. In the field of SA, the synergy of Applied Linguistics and Artificial Intelligence (AI) has led to a robust method that goes beyond conventional methods. By incorporating linguistic principles into AI methods, this interdisciplinary collaboration allows a more nuanced perception of human sentiments expressed in language. Applied Linguistics offers the theoretical basis for understanding the details of pragmatics, semantics, and linguistic structures, while AI algorithms leverage this knowledge for analyzing large datasets with notable accuracy. This study presents an Applied Linguistics-driven Artificial intelligence Approach for SA and Classification (ALAIA-SAC) system in social media. The primary intention of the ALAIA-SAC technique is to apply an attention mechanism with a fractal hyperparameter-tuned deep learning (DL) method for identifying sentiments. In the ALAIA-SAC technique, data preprocessing takes place in several stages to convert the input data into a compatible format. In addition, the TF-IDF model could be employed for the word embedding method. The self-attention directional long short-term memory (SBiLSTM) model is used for sentiment classification. Finally, the SBiLSTM model’s hyperparameter selection is performed using a Fractal Pelican optimization algorithm (FPOA). The experimentation results of the ALAIA-SAC method are assessed under two benchmark datasets. The comparative study of the ALAIA-SAC technique exhibited a superior accuracy value of 99.17% and 99.39% under Twitter US Airlines and IMDB datasets.

Keywords: Sentiment Analysis; Artificial Intelligence; Social Media; Natural Language Processing; Fractal Pelican Optimization Algorithm (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1142/S0218348X25400158

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