Application of text mining for the metric analysis of energy saving of the Seventh Framework Programme Articles uri icon

publication date

  • June 2018

start page

  • 149

end page

  • 163

International Standard Serial Number (ISSN)

  • 1683-8947


  • Objectives. This study analyses the scientific and technological activity on energy saving through the projects of the Seventh Framework Programme. The European CORDIS database is used and text mining tools are used for content analysis. Design/Methodology/Focus. The work is developed in two phases. The first uses a scientometric approach to obtain information on: countries participating and leading the projects, distribution of funding and the relationship between the number of participants and the funds obtained. The second phase focuses on the analysis of the frequency of terms to identify the main topics of the projects. Results/Discussion. 256 projects have been awarded with the largest participants: Germany, France, United Kingdom, Spain and Italy. The funding received is very variable and there is no relationship between the number of countries and institutions per project and the amount achieved. The content analysis shows that among the main topics are those related to Electric Power Management; Obtaining energy from alternative sources (wind and solar); Electric Vehicles and Energy Efficiency. Conclusions. The most active countries in this field are the large producers. There is no relationship between the size of the research teams and the funding raised as there are projects with few participating countries and institutions and high funding. The topics analyzed by the projects are very varied and there are some specific ones such as the Electric Vehicle and others related to energy management and urban efficiency. Originality/Value. The application of mixed methodologies for the study of a scientific field is presented as a promising approach to describe the area and analyze its dynamics.


  • energy saving; seventh framework programme; information metrics studies; text mining