Monitoring and forecasting the development trends of nanogenerator technology using citation analysis and text mining

The rapid development of nanogenerator technology is seen as potentially having a major impact on people's lives and would transform the existing energy industry. Monitoring the emergence of nanogenerator technology is essential to understanding and detecting its changing trends at early stages. This information is crucial for academic and government research, the development of strategic planning, social investment and enterprise practices. Therefore, this paper proposes a framework that uses academic papers and patents as data resources and integrates citation analysis and text mining to monitor the evolutionary path of nanogenerator technology and forecast its trends. We began by using citation analysis to mine the technical knowledge contained in academic papers and to monitor the evolutionary path of nanogenerator technology. This was followed by employing the applied Hierarchical Dirichlet Process (HDP) topic model which is a kind of text mining method used to extract the technical topics contained in academic papers. We then analyzed the differences from the results of the HDP topic model and citation analysis to improve the evolutionary path of nanogenerator technology missing details due to the time lag of citation analysis. Further application of the HDP topic model allowed us to extract the technical topics contained in patents. Finally, we carried out the analysis of gaps between science and technology, combining them with expert knowledge and the evolutionary path of nanogenerator technology to forecast development trends. This paper contributes to our understanding of how nanogenerator technology emerges and develops and will be of specific interest to nanogenerator technology R&D experts.