Analysis of studies in the field of cloud computing using the latent dirichlet allocation (LDA) model

Cloud computing plays a critical role across business sectors, academic research, and personal applications. This thesis aims to identify prominent themes in the cloud computing literature and to analyze temporal trends within the field. For this purpose, a comprehensive dataset comprising 18,689 articles published between 2014 and 2024 was retrieved from the Scopus database. The methodology of the study encompasses data collection, preprocessing, and subsequently, text mining and topic modeling processes. Analyses were conducted using the Latent Dirichlet Allocation (LDA) algorithm to uncover thematic structures, conceptual clusters, and the evolution of research trends in cloud computing. The findings reveal dominant research themes such as emerging technologies and distributed applications, alongside the identification of strategic and developing areas like healthcare systems and blockchain-based solutions. Additionally, the analysis of thematic intensities across years illustrates the evolving directions of cloud computing research. By offering a content-driven conceptual mapping of the field, this thesis contributes to identifying current research gaps and provides strategic guidance for future academic studies in cloud computing.