Early Identification of Potential Disruptive Technologies Using Machine Learning and Text Mining

Early identification of potential disruptive technologies is critical to corporate R&D investment decisions and government R&D strategy decisions. However, how early identifying potential disruptive technologies has been the focus of academic community. Therefore, in this paper, we propose a framework for early identification of potential disruptive technologies based on machine learning and text mining. In the framework, we firstly obtain relationships pattern between the characteristics of highly cited papers and their citation trends by using machine learning models. Then, we use the relationships pattern to identify potential highly cited papers, solving the time lag problem of using citation analysis to identify potential highly cited papers. Secondly, we construct a breakthrough index based on breakthrough research characteristics to identify breakthrough papers from potential highly cited papers. Finally, we use text mining methods to obtain breakthrough research topics from breakthrough papers, and identify potential disruptive technologies by analyzing and evaluating breakthrough research topics. An empirical study was conducted in the field of chemistry discipline to verify the framework's feasibility and effectiveness. This paper provides a new perspective for the early identification of potential disruptive technologies and lessons for breakthrough research identification and evaluation.