Machine Learning-Based Sentiment Analysis Pipeline for Evaluating Hajj Food Service Quality

Pilgrimage, also known as Hajj, brings together millions of people each year, creating significant challenges in managing, organizing, and maintaining the quality of various services. Among these essential services, food provision plays a vital role in shaping pilgrims’ overall experience and satisfaction. Despite its importance, research focusing on food services using sentiment analysis during Hajj remains limited. Existing studies often rely on social media data, which may not accurately capture the genuine opinions of pilgrims. This study addresses this gap by analyzing food service text reviews collected from Google Maps within the Hajj context. It contributes a new dataset collected for evaluating food services provided to pilgrims after the Hajj season, along with an empirical benchmark for Arabic Hajj food reviews. The dataset consists of 4,018 Google Maps reviews from 160 Hajj campaigns conducted between 2022 and 2025. After data preprocessing, the reviews were classified using several classical machine learning algorithms as empirical baselines, including support vector machine (SVM), logistic regression (LR), Naïve Bayes (NB), decision tree (DT), and random forest (RF). The experimental results demonstrate that LR achieved the highest accuracy of 93.6% among the evaluated models, followed by SVM and RF with accuracies of 92.9% and 92.2%, respectively. The analysis also shows that positive sentiment dominated across all studied years, indicating an overall improvement in pilgrims’ satisfaction with food services. However, the persistence of food-related issues highlights the need for continued attention and improvement in service quality.

@article{Almutairi2026,
  title     = {Machine Learning-Based Sentiment Analysis Pipeline for Evaluating Hajj Food Service Quality},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {17},
  number    = {1},
  year      = {2026},
  publisher = {The Science and Information Organization},
  author    = {Amjad Enad Almutairi and Aisha Yaquob Alsobhi and Abdulrhman M Alshareef},
  doi       = {10.14569/IJACSA.2026.0170195},
  url       = {https://doi.org/10.14569/IJACSA.2026.0170195}
}