Exploring diverse interests of collaborators in smart cities: A topic analysis using LDA and BERT

Smart cities have emerged as a promising solution to the problems associated with urbanization. However, research that holistically considers diverse stakeholders in smart cities is scarce. This study utilizes data from four types of collaborators (academia, public sector, industry, and civil society actors) to identify key topics and suggest research areas for developing smart cities. We used latent Dirichlet allocation and Bidirectional Encoder Representations from Transformers for topic extraction and analysis. The analysis reveals that sustainability and digital platform have received similar levels of interest from academia, industry, and government, whereas governance, resource, and green space are less frequently mentioned than technology-related topics. Hype cycle analysis, which considers public and media expectations, reveals that smart cities experienced rapid growth from 2015 to 2021, but the growth rate has slowed since 2022. This means that a breakthrough improvement in the current situation is required. Accordingly, we propose resolving the unbalanced distribution of topic interests among collaborators, especially in the areas of governance, environment, economy, and healthcare. We expect that our findings will help researchers, policymakers, and industry stakeholders in understanding which topics are underdeveloped in their fields and taking active measures for the future development of smart cities.