Mining changes of user expectations over time from online reviews

[+] Author and Article Information
Tianjun Hou

9-9 bis, 9 ter rue Joliot Curie - 91 190 Gif-sur-Yvette Gif-sur-Yvette, Ile de France 91190 France tianjun.hou@centralesupelec.fr

Bernard Yannou

Université Paris-Saclay, CentraleSupélec n/a Chatenay-Malabry, 92290 France bernard.yannou@centralesupelec.fr

Yann Leroy

9-9 bis, 9 ter rue Joliot Curie - 91 190 Gif-sur-Yvette Gif-sur-Yvette, Ile de France 91 190 France yann.leroy@centralesupelec.fr

Emilie Poirson

1, rue de la Noë Nantes, 44321 France emilie.poirson@ec-nantes.fr

1Corresponding author.

Contributed by the Design Theory and Methodology Committee of ASME for publication in the Journal of Mechanical Design. Manuscript received October 5, 2018; final manuscript received January 22, 2019; published online xx xx, xxxx. Assoc. Editor: Katja Holtta-Otto.

ASME doi:10.1115/1.4042793 History: Received October 05, 2018; Accepted January 22, 2019


Velocity–one of the characteristics of the online review data–describes the rapid speed at which the data is generated. With this characteristic, designers can monitor change in customer feedbacks to help set up product improvement strategies. Here we propose an approach for capturing changes in user preference for product affordances based on online reviews. First, the approach uses a rule-based natural language processing method to automatically identify and structure product affordances from review text. Then, conjoint analysis is used to quantitatively categorize the structured affordances in the Kano model. Finally, changes in user preference can be found by applying the conjoint analysis on the online reviews posted in different time-spans. A case study based on the online reviews of Kindle e-readers downloaded from amazon.com shows that designers can use our proposed approach to evaluate their product improvement strategies for previous products and develop new product improvement strategies for future products.

Copyright © 2019 by ASME
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