Hostname: page-component-7479d7b7d-k7p5g Total loading time: 0 Render date: 2024-07-10T20:04:48.087Z Has data issue: false hasContentIssue false
  • English
  • Français

An Affordance-Based Online Review Analysis Framework

Part of: Knowledge-based Engineering

Published online by Cambridge University Press:  26 July 2019

Tianjun Hou ,
Bernard Yannou ,
Yann Leroy  and
Emilie Poirson
Tianjun Hou*
Affiliation:
Laboratoire Genie Industriel, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France
Bernard Yannou
Affiliation:
Laboratoire Genie Industriel, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France
Yann Leroy
Affiliation:
Laboratoire Genie Industriel, CentraleSupélec, Université Paris-Saclay, Gif-sur-Yvette, France
Emilie Poirson
Affiliation:
LS2N, Ecole Centrale de Nantes, Nantes, France
*
Contact: Hou, Tianjun, Laboratoire G, nie Industriel, CentraleSupelec, France, tianjun.hou@centralesupelec.fr

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

One of the main tasks of today's data-driven design is to learn customers' concerns from the feedback data posted on the internet, to drive smarter and more profitable decisions during product development. Feature-based opinion mining was first performed by the computer and design scientists to analyse online product reviews. In order to provide more sophisticated customer feedback analyses and to understand in a deeper way customer concerns about products, the authors propose an affordance-based online review analysis framework. This framework allows understanding how and in what condition customers use their products, how user preferences change over years and how customers use the product innovatively. An empirical case study using the proposed approach is conducted with the online reviews of Kindle e-readers downloaded from amazon.com. A set of innovation leads and redesign paths are provided for the design of next-generation e-reader. This study suggests that bridging data analytics with classical models and methods in design engineering can bring success for data-driven design.

Keywords

Big dataDesign engineeringInnovation
Type
Article
Information
Proceedings of the Design Society: International Conference on Engineering Design , Volume 1 , Issue 1 , July 2019 , pp. 2457 - 2466
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - ND
This is an Open Access article, distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives licence (http://creativecommons.org/licenses/by-nc-nd/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is unaltered and is properly cited. The written permission of Cambridge University Press must be obtained for commercial re-use or in order to create a derivative work.
Copyright
© The Author(s) 2019

References

Brown, D.C. and Maier, J.R. (2015), “Affordances in design”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 29, pp. 231–234.10.1017/S0890060415000244Google Scholar
Chen, Y., Zhao, Y., Qin, B. and Liu, T. (2016), “Product Aspect Clustering by Incorporating Background Knowledge for Opinion Mining”, PloS one, Vol. 11, p. e0159901.10.1371/journal.pone.0159901Google Scholar
Chou, A. and Shu, L. (2014), “Towards extracting affordances from online consumer product reviews”, ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, pp. V007T07A030–V007T07A030.Google Scholar
Eppinger, S. and Ulrich, K. (2015), Product design and development, McGraw-Hill Higher Education.Google Scholar
Gero, J.S. and Kannengiesser, U. (2012), “Representational affordances in design, with examples from analogy making and optimization”, Research in Engineering Design, Vol. 23, pp. 235–249.10.1007/s00163-012-0128-yGoogle Scholar
Gibson, J. J. (1978), “The ecological approach to the visual perception of pictures”, Leonardo, Vol. 11, pp. 227–235.10.2307/1574154Google Scholar
Hu, J. and Fadel, G. M. (2012), “Categorizing affordances for product design”, ASME 2012 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, pp. 325–339.10.1115/DETC2012-70933Google Scholar
Jin, J., Liu, Y., Ji, P. and Liu, H. (2016), “Understanding big consumer opinion data for market-driven product design”. International Journal of Production Research, Vol. 54, pp. 3019–3041.Google Scholar
Kano, N. (1984), “Attractive quality and must-be quality”, Hinshitsu (Quality, The Journal of Japanese Society for Quality Control), Vol. 14, pp. 39–48.Google Scholar
Liu, B. (2012), “Sentiment analysis and opinion mining”, Synthesis lectures on human language technologies, Vol. 5, pp. 1–167.10.2200/S00416ED1V01Y201204HLT016Google Scholar
Maier, J. R. and Fadel, G. M. (2009), “Affordance-based design methods for innovative design, redesign and reverse engineering”, Research in Engineering Design, Vol. 20, p. 225.10.1007/s00163-009-0064-7Google Scholar
Mata, I. Fadel, G. and Mocko, G. (2015), “Toward automating affordance-based design”, Artificial Intelligence for Engineering Design, Analysis and Manufacturing, Vol. 29, pp. 297–305.10.1017/S0890060415000256Google Scholar
Mikolov, T. Chen, K., Corrado, G. and Dean, J. (2013), “Efficient estimation of word representations in vector space”, arXiv preprint arXiv: Vol. 1301 No. 3781.Google Scholar
Ravi, K. and Ravi, V. (2015), “A survey on opinion mining and sentiment analysis: Tasks, approaches and applications”, Knowledge-Based Systems, Vol. 89, pp. 14–46.10.1016/j.knosys.2015.06.015Google Scholar
Tuarob, S. and Tucker, C. S. (2014), “Discovering next generation product innovations by identifying lead user preferences expressed through large scale social media data”. ASME 2014 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. , pp. V01BT02A008–V01BT02A008.10.1115/DETC2014-34767Google Scholar
Urban, G. L. and Hauser, J. R. (2004), “Listening in to find and explore new combinations of customer needs”, Journal of Marketing, Vol. 68. No. 2, pp. 72–7810.1509/jmkg.68.2.72.27793Google Scholar
Yannou, B., Yvars, P.-A., Hoyle, C. and Chen, W. (2013), “Set-based design by simulation of usage scenario coverage”. Journal of Engineering Design, Vol. 24, pp. 575–603.10.1080/09544828.2013.780201Google Scholar
Zhang, H., Sekhari, A., Ouzrout, Y. and Bouras, A. (2016), “Jointly identifying opinion mining elements and fuzzy measurement of opinion intensity to analyze product features”, Engineering Applications of Artificial Intelligence, Vol. 47, pp. 122–139.10.1016/j.engappai.2015.06.007Google Scholar