Unveiling Sentiments: A Comprehensive Analysis of Arabic Hajj-Related Tweets from 2017–2022 Utilizing Advanced AI Models

Abstract

Sentiment analysis plays a crucial role in understanding public opinion and social media trends. It involves analyzing the emotional tone and polarity of a given text. When applied to Arabic text, this task becomes particularly challenging due to the language’s complex morphology, right-to-left script, and intricate nuances in expressing emotions. Social media has emerged as a powerful platform for individuals to express their sentiments, especially regarding religious and cultural events. Consequently, studying sentiment analysis in the context of Hajj has become a captivating subject. This research paper presents a comprehensive sentiment analysis of tweets discussing the annual Hajj pilgrimage over a six-year period. By employing a combination of machine learning and deep learning models, this study successfully conducted sentiment analysis on a sizable dataset consisting of Arabic tweets. The process involves pre-processing, feature extraction, and sentiment classification. The objective was to uncover the prevailing sentiments associated with Hajj over different years, before, during, and after each Hajj event. Importantly, the results presented in this study highlight that BERT, an advanced transformer-based model, outperformed other models in accurately classifying sentiment. This underscores its effectiveness in capturing the complexities inherent in Arabic text.

Harnessing Machine Learning to Unveil Emotional Responses to Hateful Content on Social Media

“…Machine learning has been widely considered to solve problems related to behaviors, such as traffic behavior [39], vehicle behavior [40], and human behavior [41]. In [42], the author conducted sentiment analysis on Arabic tweets discussing the Hajj pilgrimage over six years, employing machine learning and deep learning models. The results revealed sentiment patterns before, during, and after Hajj events, with BERT emerging as the most effective model for accurately classifying sentiment in Arabic text.…”