Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies
Tingting Ma,
Xiao Zhou,
Jia Liu,
Zhenkai Lou,
Zhaoting Hua and
Ruitao Wang
Technological Forecasting and Social Change, 2021, vol. 173, issue C
Abstract:
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.
Keywords: Technological opportunities analysis; Topic extraction; Topic modeling; Semantic SAO analysis; Dye sensitized solar cell (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (11)
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Persistent link: https://EconPapers.repec.org/RePEc:eee:tefoso:v:173:y:2021:i:c:s0040162521005928
DOI: 10.1016/j.techfore.2021.121159
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