Impressions of the Community of Makkah on the Hajj in the Light of Covid-

Aldhubaib, Hani A.

doi:10.4197/eng.32-1.4

JKAU Eng Sci

2022

DOI: 10.4197/eng.32-1.4

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Impressions of the Community of Makkah on the Hajj in the Light of Covid-

Hani A. Aldhubaib¹

Abstract: Throughout history, the community of Makkah has been known to make every effort to serve the Muslims who come to perform the Hajj ritual (Pilgrimage), the fifth pillar of Islam. However, the Hajj of 1441 H (2020) was different due to the outbreak of the new Coronavirus

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2024

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Enhancing Hajj and Umrah Rituals and Crowd Management Through AI Technologies: A Comprehensive Survey of Applications and Future Directions

Shah

2024

IEEE Access

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Enhancing Hajj and Umrah Rituals and Crowd Management Through AI Technologies: A Comprehensive Survey of Applications and Future Directions

Shah

2024

IEEE Access

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Unveiling Sentiments: A Comprehensive Analysis of Arabic Hajj-Related Tweets from 2017–2022 Utilizing Advanced AI Models

Alghamdi

2024

BDCC

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Sentiment analysis plays a crucial role in understanding public opinion and social media trends. It involves analyzing the emotional tone and polarity of a given text. When applied to Arabic text, this task becomes particularly challenging due to the language’s complex morphology, right-to-left script, and intricate nuances in expressing emotions. Social media has emerged as a powerful platform for individuals to express their sentiments, especially regarding religious and cultural events. Consequently, studying sentiment analysis in the context of Hajj has become a captivating subject. This research paper presents a comprehensive sentiment analysis of tweets discussing the annual Hajj pilgrimage over a six-year period. By employing a combination of machine learning and deep learning models, this study successfully conducted sentiment analysis on a sizable dataset consisting of Arabic tweets. The process involves pre-processing, feature extraction, and sentiment classification. The objective was to uncover the prevailing sentiments associated with Hajj over different years, before, during, and after each Hajj event. Importantly, the results presented in this study highlight that BERT, an advanced transformer-based model, outperformed other models in accurately classifying sentiment. This underscores its effectiveness in capturing the complexities inherent in Arabic text.

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Sentiment analysis of pilgrims using CNN-LSTM deep learning approach

Alasmari,

Farooqi,

Alotaibi

2024

PeerJ Computer Science

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Crowd management refers to the management and control of masses at specific locations. A Hajj gathering is an example. Hajj is the biggest gathering of Muslims worldwide. Over two million Muslims from all over the globe come annually to Makkah, Saudi Arabia. Authorities of Saudi Arabia strive to provide comfortable comprehensive services to pilgrims using the latest modern technologies. Recent studies have focused on camera scenes and live streaming to assess the count and monitor the behavior of the crowd. However, the opinions of the pilgrims and their feelings about their experience of Hajj are not well known, and the data on social media (SM) is limited. This paper provides a combination of Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) algorithms for sentiment analysis of pilgrims using a novel and specialized dataset, namely Catering-Hajj. The model is based on four CNN layers for local feature extraction after the One-Hot Encoder, and one LSTM layer to maintain long-term dependencies. The generated feature maps are passed to the SoftMax layer to classify final outputs. The proposed model is applied to a real case study of issues related to pre-prepared food at Hajj 1442. Started with collecting the dataset, extracting target attitudes, annotating the data correctly, and analyzing the positive, negative, and neutral attitudes of the pilgrims to this event. Our model is compared with a set of Machine Learning (ML) models including Support Vector Machine (SVM), Logistic Regression (LR), Decision Tree (DT), and Random Forest (RF), as well as CNN and LSTM models. The experimental results show that SVM, RF, and LSTM achieve the same rate of roughly 81%. LR and CNN achieve 79%, and DT achieves 71%. The proposed model outperforms other classifiers on our dataset by 92%.

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Impressions of the Community of Makkah on the Hajj in the Light of Covid-

Abstract: Throughout history, the community of Makkah has been known to make every effort to serve the Muslims who come to perform the Hajj ritual (Pilgrimage), the fifth pillar of Islam. However, the Hajj of 1441 H (2020) was different due to the outbreak of the new Coronavirus

Cited by 3 publications

References 0 publications

Enhancing Hajj and Umrah Rituals and Crowd Management Through AI Technologies: A Comprehensive Survey of Applications and Future Directions

Enhancing Hajj and Umrah Rituals and Crowd Management Through AI Technologies: A Comprehensive Survey of Applications and Future Directions

Unveiling Sentiments: A Comprehensive Analysis of Arabic Hajj-Related Tweets from 2017–2022 Utilizing Advanced AI Models

Sentiment analysis of pilgrims using CNN-LSTM deep learning approach

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