INTEGRATING FRACTAL SNOW ABLATION OPTIMIZER WITH BAYESIAN MACHINE LEARNING FOR ASPECT-LEVEL SENTIMENT ANALYSIS ON SOCIAL MEDIA

Shouki A. Ebad, Wala Bin Subait, Nadhem Nemri, Wafa Sulaiman Almukadi, Nasser Aljohani, Ayman Yafoz, Raed Alsini and Mesfer Al Duhayyim
Additional contact information
Shouki A. Ebad: Department of Computer Science, Faculty of Science, Northern Border University, Arar 91431, Saudi Arabia
Wala Bin Subait: ��Department of Language Preparation, Arabic Language Teaching Institute, Princess Nourah bint Abdulrahman University, P. O. Box 84428, Riyadh 11671, Saudi Arabia
Nadhem Nemri: ��Department of Information Systems, Applied College at Mahayil, King Khalid University, Abha, Saudi Arabia
Wafa Sulaiman Almukadi: �Department of Software Engineering, College of Engineering and Computer Science, University of Jeddah, Jeddah, Saudi Arabia
Nasser Aljohani: �Department of Information Systems, Faculty of Computer and Information Systems, Islamic University of Madinah, Medina 42351, Saudi Arabia
Ayman Yafoz: ��Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Raed Alsini: ��Department of Information Systems, Faculty of Computing and Information Technology, King Abdulaziz University, Jeddah, Saudi Arabia
Mesfer Al Duhayyim: *Department of Computer Science, College of Computer Engineering and Sciences, Prince Sattam bin Abdulaziz University, Al-Kharj 16273, Saudi Arabia

FRACTALS (fractals), 2024, vol. 32, issue 09n10, 1-18

Abstract: Social media platforms have become vast repositories of user-generated content, offering an abundant data source for sentiment analysis (SA). SA is a natural language processing (NLP) algorithm that defines the sentiment or emotional tone expressed in the given text. It includes utilizing computational techniques to automatically detect and categorize the sentiment as negative, positive, or neutral. Aspect-based SA (ABSA) systems leverage machine learning (ML) approaches to discriminate nuanced opinions within the text, which break down sentiment through particular attributes or aspects of the subject matter. Businesses and researchers can gain deep insights into brand perception, public opinion, and product feedback by integrating social media data with ABSA methodologies. This enables the extraction of sentiment polarity and more actionable and targeted insights. By applying ML approaches trained on the abundance of social media data, organizations can identify areas for improvement, tailor their strategies to meet their audience’s evolving needs and preferences and better understand customer sentiments. In this view, this study develops a new Fractal Snow Ablation Optimizer with Bayesian Machine Learning for Aspect-Level Sentiment Analysis (SAOBML-ALSA) technique on social media. The SAOBML-ALSA approach examines social media content to identify sentiments into distinct classes. In the primary stage, the SAOBML-ALSA technique preprocesses the input social media content to transform it into a meaningful format. This is followed by a LeBERT-based word embedding process. The SAOBML-ALSA technique applies a Naïve Bayes (NB) classifier for ALSA. Eventually, the parameter selection of the NB classifier will be done using the SAO technique. The performance evaluation of the SAOBML-ALSA methodology was examined under the benchmark database. The experimental results stated that the SAOBML-ALSA technique exhibits promising performance compared to other models.

Keywords: Aspect-Based Sentiment Analysis; Naïve Bayes; Social Media; Machine Learning; Fractal Snow Ablation Optimizer; NLP (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0218348X25400250
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wsi:fracta:v:32:y:2024:i:09n10:n:s0218348x25400250

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0218348X25400250

Access Statistics for this article

FRACTALS (fractals) is currently edited by Tara Taylor

More articles in FRACTALS (fractals) from World Scientific Publishing Co. Pte. Ltd.
Bibliographic data for series maintained by Tai Tone Lim ().