Abstract
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%.