UBIQUITOUS LEARNING-DRIVEN NATURAL LANGUAGE PROCESSING FOR TWITTER CLIMATE CHANGE SENTIMENT ANALYSIS USING BAYESIAN MACHINE LEARNING FOR FOSTER SUSTAINABLE DEVELOPMENT IN RURAL AREAS

Majdy M. Eltahir, Wala Bin Subait, Saad Alahmari, Abeer A. K. Alharbi, Ahmed S. Salama and Amani A. Alneil
Additional contact information
Majdy M. Eltahir: Department of Information Systems, Applied College at Mahayil, King Khalid University, 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
Saad Alahmari: ��Department of Computer Science, Applied College, Northern Border University, Arar, Saudi Arabia
Abeer A. K. Alharbi: �Department Information Systems, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh 11432, Saudi Arabia
Ahmed S. Salama: �Department of Electrical Engineering, Faculty of Engineering & Technology, Future University in Egypt, New Cairo 11845, Egypt
Amani A. Alneil: ��Department of Computer and Self Development, Preparatory Year Deanship, Prince Sattam bin Abdulaziz University, AlKharj, Saudi Arabia

FRACTALS (fractals), 2025, vol. 33, issue 02, 1-19

Abstract: The size of data shared has promptly surged with the usage of social networks, and this has become a crucial research area for environmental concerns. Sentiment analysis (SA) defines the behavior and people’s sensitivity to environmental problems. Ubiquitous learning and natural language processing (NLP) intersect to address climate change SA by analyzing vast textual data from scientific reports, social media, and news to gauge public attitudes and perceptions. This provides meaningful insight for organizations and policymakers working on climate change mitigation systems in rural areas and allows local communities to engage in environmental discourse actively, fostering a bottom-up algorithm to address climate problems. This study develops a new twitter climate change sentiment analysis using the Bayesian machine learning (TCCSA-BML) technique to promote sustainable development in rural areas. This technique exploits ubiquitous learning with NLP technologies to identify climate change in rural areas. Also, the TCCSA-BML technique undergoes data preprocessing in several ways to make the input data compatible with processing. Besides, the TCCSA-BML technique utilizes the TF-IDF model for the word embedding process. Moreover, the classification of various kinds of sentiments occurs using the Bayesian model averaging (BMA) technique comprising three classifiers, namely attention long short-term memory (ALSTM), extreme learning machine (ELM), and gated recurrent unit (GRU). Finally, the parameter tuning of the classifier is implemented by the coyote optimization algorithm (COA) model. The performance analysis of the TCCSA-BML approach is evaluated on the Kaggle SA dataset. The experimental validation of the TCCSA-BML approach portrayed a superior accuracy value of 94.07% over other models.

Keywords: Twitter; NLP; Coyote Optimization Algorithm; Climate Change; Sentiment Analysis; Bayesian Machine Learning (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.worldscientific.com/doi/abs/10.1142/S0218348X25400420
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:33:y:2025:i:02:n:s0218348x25400420

Ordering information: This journal article can be ordered from

DOI: 10.1142/S0218348X25400420

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 ().