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

Abstract

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.