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Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)

Hajj (pilgrimage) is a unique social and religious event in which many Muslims worldwide come to perform Hajj. More than two million people travel to Makkah, Saudi Arabia annually to perform various Hajj rituals for four to five days. However, given the recent outbreak of the coronavirus (COVID-19)...

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Autor principal: Shambour, Mohd Khaled
Formato: Online Artículo Texto
Lenguaje:English
Publicado: PeerJ Inc. 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575857/
https://www.ncbi.nlm.nih.gov/pubmed/36262123
http://dx.doi.org/10.7717/peerj-cs.1087
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description Hajj (pilgrimage) is a unique social and religious event in which many Muslims worldwide come to perform Hajj. More than two million people travel to Makkah, Saudi Arabia annually to perform various Hajj rituals for four to five days. However, given the recent outbreak of the coronavirus (COVID-19) and its variants, Hajj in the last 2 years 2020–2021 has been different because pilgrims were limited down to a few thousand to control and prevent the spread of COVID-19. This study employs a deep learning approach to investigate the impressions of pilgrims and others from within and outside the Makkah community during the 1442 AH Hajj season. Approximately 4,300 Hajj-related posts and interactions were collected from social media channels, such as Twitter and YouTube, during the Hajj season Dhul-Hijjah 1–13, 1442 (July 11–23, 2021). Convolutional neural networks (CNNs) and long short-term memory (LSTM) deep learning methods were utilized to investigate people’s impressions from the collected data. The CNN-LSTM approach showed superior performance results compared with other widely used classification models in terms of F-score and accuracy. Findings revealed significantly positive sentiment rates for tweets collected from Mina and Arafa holy sites, with ratios exceeding 4 out of 5. Furthermore, the sentiment analysis (SA) rates for tweets about Hajj and pilgrims varied during the days of Hajj. Some were classified as positive tweets, such as describing joy at receiving the days of Hajj, and some were negative tweets, such as expressing the impression about the hot weather and the level of satisfaction for some services. Moreover, the SA of comments on several YouTube videos revealed positive classified comments, including praise and supplications, and negative classified comments, such as expressing regret that the Hajj was limited to a small number of pilgrims.
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spelling pubmed-95758572022-10-18 Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021) Shambour, Mohd Khaled PeerJ Comput Sci Artificial Intelligence Hajj (pilgrimage) is a unique social and religious event in which many Muslims worldwide come to perform Hajj. More than two million people travel to Makkah, Saudi Arabia annually to perform various Hajj rituals for four to five days. However, given the recent outbreak of the coronavirus (COVID-19) and its variants, Hajj in the last 2 years 2020–2021 has been different because pilgrims were limited down to a few thousand to control and prevent the spread of COVID-19. This study employs a deep learning approach to investigate the impressions of pilgrims and others from within and outside the Makkah community during the 1442 AH Hajj season. Approximately 4,300 Hajj-related posts and interactions were collected from social media channels, such as Twitter and YouTube, during the Hajj season Dhul-Hijjah 1–13, 1442 (July 11–23, 2021). Convolutional neural networks (CNNs) and long short-term memory (LSTM) deep learning methods were utilized to investigate people’s impressions from the collected data. The CNN-LSTM approach showed superior performance results compared with other widely used classification models in terms of F-score and accuracy. Findings revealed significantly positive sentiment rates for tweets collected from Mina and Arafa holy sites, with ratios exceeding 4 out of 5. Furthermore, the sentiment analysis (SA) rates for tweets about Hajj and pilgrims varied during the days of Hajj. Some were classified as positive tweets, such as describing joy at receiving the days of Hajj, and some were negative tweets, such as expressing the impression about the hot weather and the level of satisfaction for some services. Moreover, the SA of comments on several YouTube videos revealed positive classified comments, including praise and supplications, and negative classified comments, such as expressing regret that the Hajj was limited to a small number of pilgrims. PeerJ Inc. 2022-09-20 /pmc/articles/PMC9575857/ /pubmed/36262123 http://dx.doi.org/10.7717/peerj-cs.1087 Text en © 2022 Shambour https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Computer Science) and either DOI or URL of the article must be cited.
spellingShingle Artificial Intelligence
Shambour, Mohd Khaled
Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)
title Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)
title_full Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)
title_fullStr Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)
title_full_unstemmed Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)
title_short Analyzing perceptions of a global event using CNN-LSTM deep learning approach: the case of Hajj 1442 (2021)
title_sort analyzing perceptions of a global event using cnn-lstm deep learning approach: the case of hajj 1442 (2021)
topic Artificial Intelligence
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9575857/
https://www.ncbi.nlm.nih.gov/pubmed/36262123
http://dx.doi.org/10.7717/peerj-cs.1087
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