Applying Text Analytics Methodology to Analyze Project Reports

The goal of this article is to develop a support methodology for Driving Urban Transition (DUT) partnership to ensure that the knowledge gathered from ERA-NET Urban Accessibility and Connectivity (EN-UAC, 2023) projects, to repeatedly identify the requirements of programme entities and define specific topics for future calls. Fifteen projects under the Horizon 2020 ERA-NET initiative have been analysed to detect similarity between projects, uniqueness of the projects, project compliance with DUT and SRIA, and gap between projects and DUT, SRIA methodology. A particular focus in the analysis was on the project "Individual Mobility Budgets as a Foundation for Social and Ethical Carbon Reduction"(MyFairShare). Text mining methods were used for documents analysis. The similarity between the documents detected by the cluster algorithm and they were compared using words, as a result, the documents were combined into three clusters: "Strategy implementation and network infrastructure"; "Transport accessibility and policy"and "Urban city mobility". The identification of unique terms shown the terms energy, ecosystem and climate are unique for DUT&SRIA and are not found in 15 EN-UAC project applications and the next specific topics for future calls can be within the subject of energy, climate and ecosystem.