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Technology opportunity discovery by structuring user needs based on natural language processing and machine learning

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  • Taeyeoun Roh
  • Yujin Jeong
  • Hyejin Jang
  • Byungun Yoon

Abstract

Discovering technology opportunities from the opinion of users can promote successful technological development by satisfying the needs of users. However, although previous approaches using opinion mining only have classified various needs of users into positive or negative categories, they cannot derive the main reasons for their opinion. To solve this problem, this research proposes an approach to exploring technology opportunity by structuring user needs with a concept of opinion trigger of objects and functions of the technology-based products. To discover technology opportunity, first, an opinion trigger is identified from review data using Naïve Base classifier and natural language processing. Second, the opinion triggers and patent keywords that have a similar meaning in context are clustered to discover the needs of the user and need-related technology. Then, the sentimental values of needs are calculated through graph-based semi-supervised learning. Finally, the needs of the user are classified in resolving the problem of vacant technology to discover technology opportunity. Then, an R&D strategy of each opportunity is suggested based on opinion triggers, patent keywords, and their property. Based on the concept of opinion trigger-based methodology, a case study is conducted on automobile—related reviews, extracting the customer needs and presenting important R&D projects such as an extracted need (cargo transportation) and its R&D strategy (resolving contradiction). The proposed approach can analyze the needs of user at a functional level to discover new technology opportunities.

Suggested Citation

  • Taeyeoun Roh & Yujin Jeong & Hyejin Jang & Byungun Yoon, 2019. "Technology opportunity discovery by structuring user needs based on natural language processing and machine learning," PLOS ONE, Public Library of Science, vol. 14(10), pages 1-27, October.
    • Handle: RePEc:plo:pone00:0223404
    DOI: 10.1371/journal.pone.0223404
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    1. Jihong Chen & Kai Zhang & Yuan Zhou & Yufei Liu & Lingfeng Li & Zheng Chen & Li Yin, 2019. "Exploring the Development of Research, Technology and Business of Machine Tool Domain in New-Generation Information Technology Environment Based on Machine Learning," Sustainability, MDPI, vol. 11(12), pages 1-38, June.
    2. Li, Xin & Xie, Qianqian & Jiang, Jiaojiao & Zhou, Yuan & Huang, Lucheng, 2019. "Identifying and monitoring the development trends of emerging technologies using patent analysis and Twitter data mining: The case of perovskite solar cell technology," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 687-705.
    3. Giada Di Stefano & Alfonso Gambardella & Gianmario Verona, 2012. "Technology Push and Demand Pull Perspectives in Innovation Studies: Current Findings and Future Research Directions," Post-Print hal-00696607, HAL.
    4. D. Thorleuchter & D. Van Den Poel & A. Prinzie, 2011. "Analyzing existing customers’ websites to improve the customer acquisition process as well as the profitability prediction in B-to-B marketing," Working Papers of Faculty of Economics and Business Administration, Ghent University, Belgium 11/733, Ghent University, Faculty of Economics and Business Administration.
    5. Altuntas, Serkan & Dereli, Turkay & Kusiak, Andrew, 2015. "Analysis of patent documents with weighted association rules," Technological Forecasting and Social Change, Elsevier, vol. 92(C), pages 249-262.
    6. Janghyeok Yoon & Kwangsoo Kim, 2011. "Identifying rapidly evolving technological trends for R&D planning using SAO-based semantic patent networks," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(1), pages 213-228, July.
    7. Higgins, Christopher D. & Mohamed, Moataz & Ferguson, Mark R., 2017. "Size matters: How vehicle body type affects consumer preferences for electric vehicles," Transportation Research Part A: Policy and Practice, Elsevier, vol. 100(C), pages 182-201.
    8. Hsin-Ning Su, 2017. "Global Interdependence of Collaborative R&D-Typology and Association of International Co-Patenting," Sustainability, MDPI, vol. 9(4), pages 1-28, April.
    9. Lee, Woo Jin & Sohn, So Young, 2014. "Patent analysis to identify shale gas development in China and the United States," Energy Policy, Elsevier, vol. 74(C), pages 111-115.
    10. Haupt, Reinhard & Kloyer, Martin & Lange, Marcus, 2007. "Patent indicators for the technology life cycle development," Research Policy, Elsevier, vol. 36(3), pages 387-398, April.
    11. Di Stefano, Giada & Gambardella, Alfonso & Verona, Gianmario, 2012. "Technology push and demand pull perspectives in innovation studies: Current findings and future research directions," Research Policy, Elsevier, vol. 41(8), pages 1283-1295.
    12. Taeyeoun Roh & Yujin Jeong & Byungun Yoon, 2017. "Developing a Methodology of Structuring and Layering Technological Information in Patent Documents through Natural Language Processing," Sustainability, MDPI, vol. 9(11), pages 1-19, November.
    13. Lee, Won Sang & Han, Eun Jin & Sohn, So Young, 2015. "Predicting the pattern of technology convergence using big-data technology on large-scale triadic patents," Technological Forecasting and Social Change, Elsevier, vol. 100(C), pages 317-329.
    14. Xuefeng Wang & Pingping Ma & Ying Huang & Junfang Guo & Donghua Zhu & Alan L. Porter & Zhinan Wang, 2017. "Combining SAO semantic analysis and morphology analysis to identify technology opportunities," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 3-24, April.
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    3. Li, Xin & Wu, Yundi & Cheng, Haolun & Xie, Qianqian & Daim, Tugrul, 2023. "Identifying technology opportunity using SAO semantic mining and outlier detection method: A case of triboelectric nanogenerator technology," Technological Forecasting and Social Change, Elsevier, vol. 189(C).

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