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

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  • Ma, Tingting
  • Zhou, Xiao
  • Liu, Jia
  • Lou, Zhenkai
  • Hua, Zhaoting
  • Wang, Ruitao

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.

Suggested Citation

  • Ma, Tingting & Zhou, Xiao & Liu, Jia & Lou, Zhenkai & Hua, Zhaoting & Wang, Ruitao, 2021. "Combining topic modeling and SAO semantic analysis to identify technological opportunities of emerging technologies," Technological Forecasting and Social Change, Elsevier, vol. 173(C).
    • Handle: RePEc:eee:tefoso:v:173:y:2021:i:c:s0040162521005928
    DOI: 10.1016/j.techfore.2021.121159
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    Cited by:

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    3. Richarz, Jan & Wegewitz, Stephan & Henn, Sarah & Müller, Dirk, 2023. "Graph-based research field analysis by the use of natural language processing: An overview of German energy research," Technological Forecasting and Social Change, Elsevier, vol. 186(PB).
    4. Yang, Zaoli & Wu, Qingyang & Venkatachalam, K. & Li, Yuchen & Xu, Bing & Trojovský, Pavel, 2022. "Topic identification and sentiment trends in Weibo and WeChat content related to intellectual property in China," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    5. Jeon, Eunji & Yoon, Naeun & Sohn, So Young, 2023. "Exploring new digital therapeutics technologies for psychiatric disorders using BERTopic and PatentSBERTa," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    6. Zhenyu Yang & Wenyu Zhang & Zhimin Wang & Xiaoling Huang, 2024. "A deep learning-based method for predicting the emerging degree of research topics using emerging index," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(7), pages 4021-4042, July.
    7. Just, Julian, 2024. "Natural language processing for innovation search – Reviewing an emerging non-human innovation intermediary," Technovation, Elsevier, vol. 129(C).
    8. Tingting Wei & Danyu Feng & Shiling Song & Cai Zhang, 2024. "An extraction and novelty evaluation framework for technology knowledge elements of patents," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7417-7442, November.
    9. Ghaffari, Mohsen & Aliahmadi, Alireza & Khalkhali, Abolfazl & Zakery, Amir & Daim, Tugrul U. & Yalcin, Haydar, 2023. "Topic-based technology mapping using patent data analysis: A case study of vehicle tires," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    10. Li, Libo & Yu, Huan & Kunc, Martin, 2024. "The impact of forum content on data science open innovation performance: A system dynamics-based causal machine learning approach," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    11. Xiaoli Cao & Xiang Chen & Lu Huang & Lijie Deng & Yijie Cai & Hang Ren, 2024. "Detecting technological recombination using semantic analysis and dynamic network analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(11), pages 7385-7416, November.

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