Abstract

This study develops a natural language processing model for sentiment analysis of Queen Rania of Jordan's speeches, focusing on her advocacy for humanitarian causes. Utilizing advanced machine learning and AI techniques, we analyze her communication about refugees and vulnerable populations to identify her advocacy strategies. By integrating technology for nuanced discourse analysis and refining traditional sentiment classifications with contextual information and expert input, we identify key themes and public sentiment trends and quantify emotional responses that may not be apparent from qualitative analysis alone. The research also examines the linguistic features of her speeches, including word choice, tone and rhetorical devices, and considers the socio-political context surrounding her messages to better understand their impact on global humanitarian issues. Given the accessibility of the speeches, a manual classification phase engaged experts to identify key terms and enhance analytic accuracy. The proposed model achieved strong performance (accuracy 87%, precision 82%, recall 80%, F1-score 81%), and after incorporating expert feedback these metrics improved (accuracy 90%, precision 88%, recall 85%, F1-score 86%). Confusion-matrix analysis showed the model reliably distinguished neutral content from positive and negative sentiment, with most misclassifications occurring between neutral and affective categories. Comparison with expert classifications found five discrepancies across empathy, urgency, and hopefulness labels, typically where the model labeled emotionally charged lines as neutral. Findings illustrate the alignment of Queen Rania’s messaging with public sentiment and underscore the role of technology-driven communication in humanitarian advocacy. We identified dominant themes such as compassion and resilience and quantified emotional responses including hope and urgency. Overall, the study aims to illuminate the effectiveness of Queen Rania's communication strategies, demonstrate how sentiment analysis and NLP can deepen understanding of public engagement, and provide actionable guidance to improve humanitarian messaging in the digital age.

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