Graph-based research field analysis by the use of natural language processing: An overview of German energy research

Jan Richarz, Stephan Wegewitz, Sarah Henn and Dirk Müller

Technological Forecasting and Social Change, 2023, vol. 186, issue PB

Abstract: To stop climate change caused by the anthropogenic greenhouse effect, the amount of research project funding to develop and demonstrate solutions for carbon emission reduction has increased. Focused topics and procedures of research projects are always presented in text-based descriptions. Natural language processing offers the possibility to process and analyze data like this. In this work, we used natural language processing to extract keywords from descriptions of energy research projects with the algorithms TextRank and TF-IDF which has not been done for this kind of data basis before. A survey-based validation showed TF-IDF to be better suited for our data basis. Extracted keywords were used to conduct a keyword network analysis and calculate static and dynamic indices of the words concerning their recent importance and development over the past two decades. Further insights are shown by allocating the research projects’ amounts of funding to the extracted keywords. We found energy research to be more focused on individual components in the past. The last years were characterized by projects on energy systems and the interaction of renewable energy technologies and their integration into existing infrastructure. Finally, the results were compared with governmental research programs and we could analyze a comprehensive agreement.

Keywords: Text mining; Keyword extraction; Network analysis; Research trends; Research funding (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (2)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:186:y:2023:i:pb:s0040162522006606

DOI: 10.1016/j.techfore.2022.122139

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