Research Papers: D3 Methods

Data-Driven Sizing Specification Utilizing Consumer Text Reviews

[+] Author and Article Information
Rushtin Chaklader

OPEN Design Lab,
Penn State University,
University Park, PA 16802
e-mail: rxc341@gmail.com

Matthew B. Parkinson

Engineering Design and
Mechanical Engineering,
Penn State University,
University Park, PA 16802
e-mail: parkinson@psu.edu

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received February 20, 2017; final manuscript received July 14, 2017; published online October 2, 2017. Assoc. Editor: Ying Liu.

J. Mech. Des 139(11), 111406 (Oct 02, 2017) (7 pages) Paper No: MD-17-1158; doi: 10.1115/1.4037476 History: Received February 20, 2017; Revised July 14, 2017

The objective of this work is to introduce a new method for determining preliminary design specifications related to human-artifact interaction. This new method uses data mining of large numbers of consumer reviews. User opinion on specific product features can be time-consuming or expensive to obtain through traditional methods including surveys, experiments, and observational studies. Data mining review text of already released products may be a potentially less time consuming and costly method. Previously established methods of determining design for human variability information from consumer reviews, such as the frequency and accuracy summation (FAS) number and subsequent manual analysis, are explored. The weighted phrase rating (WPR), a new metric which can be an automated tool to quickly analyze consumer reviews, is also introduced. It does not require manual parsing of the reviews, which extends its applicability to larger review pools. This new method is shown to quickly and economically provide information useful to the establishment of design specifications.

Copyright © 2017 by ASME
Your Session has timed out. Please sign back in to continue.


Mehta, C. R. , Gite, L. P. , Pharade, S. C. , Majumder, J. , and Pandey, M. M. , 2008, “ Review of Anthropometric Considerations for Tractor Seat Design,” Int. J. Ind. Ergon., 35(5–6), pp. 546–554. [CrossRef]
McFarland, R. A. , Damon, A. , and Stoudt, H. W. , 1958, “ Anthropometry in the Design of the Driver's Workspace,” Am. J. Phys. Anthropol., 16(1), pp. 1–23. [CrossRef] [PubMed]
Garneau, C. J. , and Parkinson, M. B. , 2009, “ Including Preference in Anthropometry-Driven Models for Design,” ASME J. Mech. Des., 131(10), p. 101006. [CrossRef]
Decker, R. , and Trusov, M. , 2010, “ Estimating Aggregate Consumer Preferences From Online Product Reviews,” Int. J. Res. Mark., 27(4), pp. 293–307. [CrossRef]
Tucker, C. S. , and Kim, H. M. , 2008, “ Optimal Product Portfolio Formulation by Merging Predictive Data Mining With Multilevel Optimization,” ASME J. Mech. Des., 130(4), p. 041103. [CrossRef]
Ferguson, T. , Greene, M. , Repetti, F. , Lewis, K. , and Behdad, S. , 2015, “ Combining Anthropometric Data and Consumer Review Content to Inform Design for Human Variability,” ASME Paper No. DETC2015-47640.
Hoyt, R. , Linnville, S. , Thaler, S. , and Moore, J. , 2016, “ Digital Family History Data Mining With Neural Networks: A Pilot Study,” Perspect. Health Inf. Manage., Winter, pp. 1–14.
Chen, H. , Honda, T. , and Yang, M. , 2013, “ Approaches for Identifying Consumer Preferences for the Design of Technology Products: A Case Study of Residential Solar Panels,” ASME J. Mech. Des., 135(6), p. 061007. [CrossRef]
Formentin, S. , Filippi, P. D. , Corno, M. , Tanelli, M. , and Savaresi, S. M. , 2013, “ Data-Driven Design of Braking Control Systems,” IEEE Trans. Control Syst. Technol., 21(1), pp. 186–193. [CrossRef]
Yin, S. , Wang, G. , and Karimi, H. , 2014, “ Data-Driven Design of Robust Fault Detection System for Wind Turbines,” Mechatronics, 24(4), pp. 298–306. [CrossRef]
Zhan, J. , Loh, H. T. , and Liu, Y. , 2009, “ Gather Customer Concerns From Online Product Reviews—A Text Summarization Approach,” Expert Syst. Appl., 36(2), pp. 2107–2115. [CrossRef]
Htay, S. , and Lynn, K. , 2013, “ Extracting Product Features and Opinion Words Using Pattern Knowledge in Customer Reviews,” Sci. World J., 2013(2013), p. 394758.
McAuley, J. , Pandey, R. , and Leskovec, J. , 2015, “ Inferring Networks of Substitutable and Complementary Products,” e-print arXiv:1506.08839.
Ma, J. , and Kim, H. , 2014, “ Continuous Preference Trend Mining for Optimal Product Design With Multiple Profit Cycles,” ASME J. Mech. Des., 136(6), p. 061002. [CrossRef]
Torii, M. , Tilak, S. S. , Doan, S. , Zisook, D. S. , and Fan, J. , 2016, “ Mining Health-Related Issues in Consumer Product Reviews by Using Scalable Text Analytics,” Biomed. Inf. Insights, 8(Suppl. 1), pp. 1–11.
Zhao, Y. , Chen, Y. , and Yao, Y. , 2008, “ User-Centered Interactive Data Mining,” Int. J. Cognit. Inf. Nat. Intell., 2(1), pp. 58–72. [CrossRef]
Lehto, M. R. , and Buck, J. R. , 2007, Introduction to Human Factors and Ergonomics for Engineers, CRC Press, Boca Raton, FL.
Amazon, 2016, “ Best Sellers in Over-Ear Headphones,” Amazon, Seattle, WA, accessed Aug. 17, 2017, https://www.amazon.com/Best-Sellers-Electronics-Over-Ear-Headphones/zgbs/electronics/12097479011/ref=zg_bs_nav_e_2_172541
Gordon, C. C. , Churchill, T. , Clauser, C. E. , Bradtmiller, B. , McConville, J. T. , Tebbetts, I. , and Walker, R. A. , 1988, “ Anthropometric Survey of U.S. Army Personnel: Methods and Summary Statistics,” U.S. Army Natick Research, Development and Engineering Center, Natick, MA, Report No. NATICK/TR-89/044.
Hsu, Y. , Huang, C. , Yo, C. , Chen, C. , and Lien, C. , 2004, “ Comfort Evaluation of Hearing Protection,” Int. J. Ind. Ergon., 33(6), pp. 543–551. [CrossRef]
Lin, J. , 2009, “ Design Optimization of Headband for Headphone,” Ph.D. thesis, Washington State University, Pullman, WA.





Some tools below are only available to our subscribers or users with an online account.

Related Content

Customize your page view by dragging and repositioning the boxes below.

Related Journal Articles
Related eBook Content
Topic Collections

Sorry! You do not have access to this content. For assistance or to subscribe, please contact us:

  • TELEPHONE: 1-800-843-2763 (Toll-free in the USA)
  • EMAIL: asmedigitalcollection@asme.org
Sign In