aspg-3-2494-1791417758 20261008000238 American Scientific Publishing Group admin@americaspg.com American Scientific Publishing Group Fusion: Practice and Applications FPA 2770-0070 2692-4048 2024 14 2 Ali S. Abed Al Sailawi College of Law, University of Misan, Amarah, Iraq Mohammad Reza Kangavari School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran In the past three years, social media has had a significant impact on our lives, including crisis management. The COVID-19 pandemic highlighted the importance of accurate information and exposed the spread of false information. This paper specifically examines the COVID-19 crisis and analyzes relevant literature to provide insights for national authorities and organizations. Utilizing social media data for crisis management poses challenges due to its unstructured nature. To overcome this, the paper proposes a comprehensive method that addresses all aspects of long-term crisis management. This method relies on labeled and structured information for accurate sentiment analysis and classification. An automated approach is presented to annotate and classify tweet texts, reducing manual labeling and improving classifier accuracy. The framework involves generating topics using Latent Dirichlet Allocation (LDA) and ranking them with a new algorithm for data annotation. The labeled text is transformed into feature representation using Bert embeddings, which can be utilized in deep learning models for categorizing textual data. The primary aim of this paper is to offer valuable insights and resources to researchers studying crisis management through social media literature, with a specific focus on high-accuracy sentiment analysis. 2024 227 243 2494 https://creativecommons.org/licenses/by/4.0/ 10.54216/FPA.140219 https://www.americaspg.com/journal/3/article/2494 https://www.americaspg.com/storage/61707417327.pdf