Analysis of Automatic Online Lead User Identification

  • Sanjin PajoEmail author
  • Paul-Armand Verhaegen
  • Dennis Vandevenne
  • Joost R. Duflou
Part of the Lecture Notes in Production Engineering book series (LNPE)


Lead user identification is a systematic approach to uncovering product development opportunities by identifying lead users, individuals or groups actively involved in modifying or developing products for personal benefit. In this paper, a systematic approach called Fast Lead User IDentification (FLUID) based on online data mining, specifically of the Twitter micro-blogging site, is proposed. Topic classification, sentiment and intent of a given tweet or user-metadata can be automatically determined using various text mining techniques. The described FLUID system makes use of such techniques to rank retrieved users based on indexes derived from well-established lead user characteristics. In the initial analysis phase collection of relevant artifacts and contextual inquiry allow for measuring impact of each index toward delineating lead users from other non-lead users. Through refinement based on statistical analysis of expert assessments the effectiveness of the FLUID system is optimized.


lead user identification data mining micro-blogging social networks 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Von Hippel, E.: Lead Users: An Important Source of Novel Product Concepts. Management Science 32, 791–805 (1986)CrossRefGoogle Scholar
  2. 2.
    Morrison, P.D., Roberts, J.H., Midgley, D.F.: The nature of lead users and measurement of leading edge status. Res. Policy 33(2), 351–362 (2004)CrossRefGoogle Scholar
  3. 3.
    Von Hippel, E.: The Source of Innovation. Oxford University Press, New York (1988)Google Scholar
  4. 4.
    Srivastava, J., Shu, L.H.: Designing Products to Encourage Conservation: Applying the Discretization Principle. In: Leveraging Technology for a Sustainable World. J. Mol. Biol., pp. 569–574. Springer, Heidelberg (1981)Google Scholar
  5. 5.
    Luthje, C., Herstatt, C.: The lead user method: An outline of empirical findings and issues for future research. R & D Management 34(5), 553–568 (2004)CrossRefGoogle Scholar
  6. 6.
    Von Hippel, E., Thomke, S., Sonnack, M.: Creating breakthroughs at 3M. Harvard Bus. Rev. 77(8), 47–57 (1999)Google Scholar
  7. 7.
    Von Hippel, E.: Democratizing Innovation. In: Democratizing Innovation. MIT Press, Cambridge (2005)Google Scholar
  8. 8.
    Belz, F., Baumbach, W.: Netnography as a Method of Lead User Identification. Creativity and Innovation Management 19(3), 304–313 (2010)CrossRefGoogle Scholar
  9. 9.
    Bartl, M.: Netnography: Einblicke in die Welt der Kunden. Planung & Analyse 5, 83–89 (2007)Google Scholar
  10. 10.
    Pang, B., Lee, L.: Opinion mining and sentiment analysis. Foundations and Trends in Information Retrieval 2(1-2), 1–135 (2008)CrossRefGoogle Scholar
  11. 11.
    Wang, A.: Don’t follow me: Spam detection in twitter. In: Int’l Conference on Security and Cryptography, SECRYPT (2010)Google Scholar
  12. 12.
    Saif, H., He, Y., Alani, H.: Alleviating data sparsity for twitter sentiment analysis. In: The 2nd Workshop on Making Sense of Microposts (2012)Google Scholar
  13. 13.
    Bifet, A., Frank, E.: Sentiment Knowledge Discovery in Twitter Streaming Data. In: Pfahringer, B., Holmes, G., Hoffmann, A. (eds.) DS 2010. LNCS, vol. 6332, pp. 1–15. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  14. 14.
    Asur, S., Huberman, B.A.: Predicting the future with social media. arXiv:1003.5699v1 {cs.CY} (2010)Google Scholar
  15. 15.
    Chung, J.E., Mustafaraj, E.: Can collective sentiment expressed on twitter predict political elections? In: Burgard, W., Roth, D. (eds.) Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence, AAAI 2011, pp. 1768–1769. AAAI Press, Menlo Park (2011)Google Scholar
  16. 16.
    Alexander, P., Patrick, P.: Twitter as Corpus for Sentiment Analysis and Opinion Mining. In: Proceedings of the 7th Conference on Language Resources and Evaluation (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Sanjin Pajo
    • 1
    Email author
  • Paul-Armand Verhaegen
    • 1
  • Dennis Vandevenne
    • 1
  • Joost R. Duflou
    • 1
  1. 1.Mechanical Engineering DepartmentKatholieke Universiteit LeuvenHeverleeBelgium

Personalised recommendations