Enhancing Hajj and Umrah Services Through Predictive Social Media Classification - Samia Allaoua Chelloug; Mohammed Saleh Ali Muthanna; Faisal Jamil; Mehdhar S. A. M. Al-Gaashani; Soha Alhelaly; Ahmed Aziz; Ammar Muthanna (IEEE Access)
Each year, millions of individuals embark on the sacred journeys of Hajj and Umrah to Saudi Arabia. Given the diverse needs of these pilgrims and the continuous efforts to enhance their experience, we propose an advanced social media classification system based on predictive deep learning. The primary objective of this system is to efficiently classify and analyze social media content related to Hajj and Umrah services. To improve the effectiveness of this classification model, we introduce a predictive optimization strategy that employs a deep neural network as the learning module and utilizes particle swarm optimization to refine the weighting parameters. Leveraging real-time data from various microblogging platforms Twitter, blogging websites, Facebook, and Instagram, our model classifies individual posts using natural language processing techniques. The classification is based on relevant attributes such as service-level scores. If the dataset contains non-English text, it is first translated into English. Tokenization and preprocessing are then applied to categorize posts into five key areas: religious rites, management, safety, well-being, and services. The labeled posts are subsequently used to train a deep learning model. By incorporating a service-level score algorithm based on the TextBlob NLP library, each post is accurately classified and utilized as a feature in a supervised machine-learning classification system. The model’s performance is evaluated using standard metrics, including F-measure, Precision, and Recall. The ultimate objective is to achieve high-accuracy classification, enabling precise evaluation and improved analysis of social media content related to the pilgrimage experience.