Topic research in fuzzy domain: Based on LDA topic modelling

Over the past few decades, the field of fuzzy research has experienced rapid growth. However, there remains a dearth of a comprehensive overview and summary of fuzzy research that provides a macro understanding of the field, particularly from the lens of topic analysis. To fill this void and capture the knowledge structure and trends in fuzzy research, this paper employs Latent Dirichlet Allocation (LDA) topic models to extract ten potential crucial scientific topics from a dataset comprising 33,957 articles published in renowned journals within the fuzzy domain. The identified topics are thoroughly discussed, and their evolution over time, as well as their distribution characteristics and dynamic trends at both journal and country/region levels, are analyzed. This research contributes to a better understanding of the distribution and trends of fuzzy research topics, providing valuable insights into the future of the field. Furthermore, the findings guide identifying appropriate journals, adjusting journal strategies, and fostering inter-national collaborations in fuzzy research.

Automated summary

This study utilizes topic models to extract ten crucial scientific topics from a large dataset of fuzzy research articles and thoroughly discusses their characteristics and trends over time at both journal and country/region levels. The findings provide valuable insights into the distribution and future development of fuzzy research topics.