Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies

  • A hybrid approach to extract technical intelligence for technological opportunities analysis.
  • To blend topic modeling, SAO semantic analysis and machine learning.
  • An optimized LDA-based topic extraction model with high accuracy.
  • A case study on dye-sensitized solar cell.

With the advancement of science and the emergence of new technologies, technology opportunities analysis has attracted increasing attention from both society and academia. This study proposes a hybrid approach to integrate topic modeling, semantic SAO analysis, machine learning, and expert judgment, identifying technological topics and potential development opportunities. The systematical methodology is applied to analyze a set of 9,883 Derwent Innovation Index (DII) patents related to the dye-sensitized solar cell to present its potential contribution of technical intelligence for R&D management. Also, how the approach is validated and optimized is illustrated. The main contributions of this paper are two-fold. First, an optimized topic extraction model with high accuracy is constructed, considering both the patent classification codes and term location. Second, we integrate the topic modeling, SAO technique, and machine learning to explore semantic relationships among technological topics represented as a suite of terms. The methodology overcomes some drawbacks of the current studies. It can be used as a powerful tool for technological opportunities analysis.

Author(s): Tingting Ma, Xiao Zhou, Jia Liu, Zhenkai Lou, Zhaoting Hua, Ruitao Wang
Organization(s): Communication University of China, Beijing Wuzi University, Xidian University, Anhui University of Technology
Source: Technological Forecasting and Social Change
Year: 2021

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