Enhancing Hajj and Umrah Services Through Predictive Social Media Classification
Each year, millions of individuals embark on the sacred journeys of Hajj and Umrah to Saudi Arabia. Given the diverse needs of these pilgrims and the continuous efforts to enhance their experience, we propose an advanced social media classification system based on predictive deep learning. The prima...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/10960302/ |
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| author | Samia Allaoua Chelloug Mohammed Saleh Ali Muthanna Faisal Jamil Mehdhar S. A. M. Al-Gaashani Soha Alhelaly Ahmed Aziz Ammar Muthanna |
| author_facet | Samia Allaoua Chelloug Mohammed Saleh Ali Muthanna Faisal Jamil Mehdhar S. A. M. Al-Gaashani Soha Alhelaly Ahmed Aziz Ammar Muthanna |
| author_sort | Samia Allaoua Chelloug |
| collection | DOAJ |
| description | Each year, millions of individuals embark on the sacred journeys of Hajj and Umrah to Saudi Arabia. Given the diverse needs of these pilgrims and the continuous efforts to enhance their experience, we propose an advanced social media classification system based on predictive deep learning. The primary objective of this system is to efficiently classify and analyze social media content related to Hajj and Umrah services. To improve the effectiveness of this classification model, we introduce a predictive optimization strategy that employs a deep neural network as the learning module and utilizes particle swarm optimization to refine the weighting parameters. Leveraging real-time data from various microblogging platforms Twitter, blogging websites, Facebook, and Instagram, our model classifies individual posts using natural language processing techniques. The classification is based on relevant attributes such as service-level scores. If the dataset contains non-English text, it is first translated into English. Tokenization and preprocessing are then applied to categorize posts into five key areas: religious rites, management, safety, well-being, and services. The labeled posts are subsequently used to train a deep learning model. By incorporating a service-level score algorithm based on the TextBlob NLP library, each post is accurately classified and utilized as a feature in a supervised machine-learning classification system. The model’s performance is evaluated using standard metrics, including F-measure, Precision, and Recall. The ultimate objective is to achieve high-accuracy classification, enabling precise evaluation and improved analysis of social media content related to the pilgrimage experience. |
| format | Article |
| id | doaj-art-8f1c3bffce094bdf9b55f2bbfac67ecf |
| institution | DOAJ |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-8f1c3bffce094bdf9b55f2bbfac67ecf2025-08-20T03:11:05ZengIEEEIEEE Access2169-35362025-01-0113672206723810.1109/ACCESS.2025.355920410960302Enhancing Hajj and Umrah Services Through Predictive Social Media ClassificationSamia Allaoua Chelloug0https://orcid.org/0000-0002-9711-0235Mohammed Saleh Ali Muthanna1https://orcid.org/0000-0002-1165-7812Faisal Jamil2Mehdhar S. A. M. Al-Gaashani3Soha Alhelaly4https://orcid.org/0000-0003-3867-9293Ahmed Aziz5https://orcid.org/0000-0003-1826-6248Ammar Muthanna6https://orcid.org/0000-0003-0213-8145Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428 Riyadh, Saudi ArabiaDepartment of International Business Management, Tashkent State University of Economics, Tashkent, UzbekistanSchool of Computing, Engineering and Intelligent Systems, Ulster University, Londonderry, U.K.School of Resources and Environment, University of Electronic Science and Technology of China, Chengdu, Sichuan, ChinaCollege of Computing and Informatics, Saudi Electronic University, Riyadh, Saudi ArabiaDepartment of Computer Science, Faculty of Computer and Artificial Intelligence, Benha University, Benha, EgyptDepartment of Applied Probability and Informatics, Peoples’ Friendship University of Russia (RUDN University), Moscow, RussiaEach year, millions of individuals embark on the sacred journeys of Hajj and Umrah to Saudi Arabia. Given the diverse needs of these pilgrims and the continuous efforts to enhance their experience, we propose an advanced social media classification system based on predictive deep learning. The primary objective of this system is to efficiently classify and analyze social media content related to Hajj and Umrah services. To improve the effectiveness of this classification model, we introduce a predictive optimization strategy that employs a deep neural network as the learning module and utilizes particle swarm optimization to refine the weighting parameters. Leveraging real-time data from various microblogging platforms Twitter, blogging websites, Facebook, and Instagram, our model classifies individual posts using natural language processing techniques. The classification is based on relevant attributes such as service-level scores. If the dataset contains non-English text, it is first translated into English. Tokenization and preprocessing are then applied to categorize posts into five key areas: religious rites, management, safety, well-being, and services. The labeled posts are subsequently used to train a deep learning model. By incorporating a service-level score algorithm based on the TextBlob NLP library, each post is accurately classified and utilized as a feature in a supervised machine-learning classification system. The model’s performance is evaluated using standard metrics, including F-measure, Precision, and Recall. The ultimate objective is to achieve high-accuracy classification, enabling precise evaluation and improved analysis of social media content related to the pilgrimage experience.https://ieeexplore.ieee.org/document/10960302/Hajj and Umrah managementdeep learning optimizationsocial media classificationpredictive modellingsentiment analysisinformation sharing |
| spellingShingle | Samia Allaoua Chelloug Mohammed Saleh Ali Muthanna Faisal Jamil Mehdhar S. A. M. Al-Gaashani Soha Alhelaly Ahmed Aziz Ammar Muthanna Enhancing Hajj and Umrah Services Through Predictive Social Media Classification IEEE Access Hajj and Umrah management deep learning optimization social media classification predictive modelling sentiment analysis information sharing |
| title | Enhancing Hajj and Umrah Services Through Predictive Social Media Classification |
| title_full | Enhancing Hajj and Umrah Services Through Predictive Social Media Classification |
| title_fullStr | Enhancing Hajj and Umrah Services Through Predictive Social Media Classification |
| title_full_unstemmed | Enhancing Hajj and Umrah Services Through Predictive Social Media Classification |
| title_short | Enhancing Hajj and Umrah Services Through Predictive Social Media Classification |
| title_sort | enhancing hajj and umrah services through predictive social media classification |
| topic | Hajj and Umrah management deep learning optimization social media classification predictive modelling sentiment analysis information sharing |
| url | https://ieeexplore.ieee.org/document/10960302/ |
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