Exploring trends and emerging topics in oceanography (1992–2021) using deep learning-based topic modeling and cluster analysis - Mingyu Han; Yuntao Zhou (npj Ocean Sustainability)
Abstract Recent advancements in data sampling, modeling, and increased collaboration among scientists have driven a substantial rise in oceanography publications. By utilizing big data and deep learning, we built a BERTopic model to perform topic modeling on 334,765 publications from the Web of Science, spanning 1992 to 2021, to examine research trends and emerging topics in oceanography. We then created a topic cluster map containing the 100 most popular topics, revealing the complex interplay of topics across different sub-fields and the interdisciplinary nature of oceanography. Key emerging topics, such as precipitation and climate, reflect the increasing focus on climate change and underscore the ocean’s vital role in addressing global environmental challenges. We also revealed geographical trends in research focus and collaboration patterns. Our study not only highlights trends and areas of growing interest but also identifies topics requiring further studies, emphasizing the importance of international collaboration for future research.