Identifying Disruptive Technology Using SAOX Semantic Analysis and Web News Data Mining: A Perspective of Technology Convergence

How to identify potential disruptive technologies as early as possible is of great significance for enterprise's R&D strategic decision-making and government's innovation policy formulation. Considering the shortcomings of the existing disruptive technology identification methods in semantic level analysis and the lack of analyses on the potential market impact assessment, this paper proposes a framework for identifying disruptive technology using SAOX semantic analysis and web news data mining from a perspective of technology convergence. In the proposed framework, we firstly use the SAOX semantic analysis method and spectral clustering to obtain technology topics with high convergence. Then, based on the novelty, mutation, and growth characteristics of disruptive technologies, we employ the SAOX semantic analysis and mutation detection algorithms to analyze the novelty and mutation of the technology topics, respectively. In addition, the growth of technology topics is monitored, and potential disruptive technology topics are determined. Secondly, we analyze and assess the market impact of potential disruptive technology topics by mining the public's cognitive contents, attentions, and expected attitudes toward potential disruptive technology topics in web news. Finally, the analysis results of the technology topic characteristics and market impact are combined to identify disruptive technologies. Nanogenerator technology is used as a case study to verify the feasibility and validity of the proposed framework. This paper contributes to our understanding of the characteristics of disruptive technology and will be of interest to nanogenerator technology R&D experts.