TY - GEN
T1 - Can machine learning tools support the identification of sustainable design leads from product reviews? Opportunities and challenges
AU - Saidani, Michael
AU - Kim, Harrison
AU - Yannou, Bernard
N1 - Publisher Copyright:
Copyright © 2021 by ASME.
PY - 2021
Y1 - 2021
N2 - The increasing number of product reviews posted online is a gold mine for designers to know better about the products they develop, by capturing the voice of customers, and to improve these products accordingly. In the meantime, product design and development have an essential role in creating a more sustainable future. With the recent advance of artificial intelligence techniques in the field of natural language processing, this research aims to develop an integrated machine learning solution to obtain sustainable design insights from online product reviews automatically. In this paper, the opportunities and challenges offered by existing frameworks – including Python libraries, packages, as well as state-of-the-art algorithms like BERT – are discussed, illustrated, and positioned along an ad hoc machine learning process. This contribution discusses the opportunities to reach and the challenges to address for building a machine learning pipeline, in order to get insights from product reviews to design more sustainable products, including the five following stages, from the identification of sustainability-related reviews to the interpretation of sustainable design leads: data collection, data formatting, model training, model evaluation, and model deployment. Examples of sustainable design insights that can be produced out of product review mining and processing are given. Finally, promising lines for future research in the field are provided, including case studies putting in parallel standard products with their sustainable alternatives, to compare the features valued by customers and to generate in fine relevant sustainable design leads.
AB - The increasing number of product reviews posted online is a gold mine for designers to know better about the products they develop, by capturing the voice of customers, and to improve these products accordingly. In the meantime, product design and development have an essential role in creating a more sustainable future. With the recent advance of artificial intelligence techniques in the field of natural language processing, this research aims to develop an integrated machine learning solution to obtain sustainable design insights from online product reviews automatically. In this paper, the opportunities and challenges offered by existing frameworks – including Python libraries, packages, as well as state-of-the-art algorithms like BERT – are discussed, illustrated, and positioned along an ad hoc machine learning process. This contribution discusses the opportunities to reach and the challenges to address for building a machine learning pipeline, in order to get insights from product reviews to design more sustainable products, including the five following stages, from the identification of sustainability-related reviews to the interpretation of sustainable design leads: data collection, data formatting, model training, model evaluation, and model deployment. Examples of sustainable design insights that can be produced out of product review mining and processing are given. Finally, promising lines for future research in the field are provided, including case studies putting in parallel standard products with their sustainable alternatives, to compare the features valued by customers and to generate in fine relevant sustainable design leads.
KW - Data-driven design
KW - Machine learning
KW - Natural language processing
KW - Product reviews
KW - Sustainable design
UR - http://www.scopus.com/inward/record.url?scp=85119988217&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85119988217&partnerID=8YFLogxK
U2 - 10.1115/DETC2021-70613
DO - 10.1115/DETC2021-70613
M3 - Conference contribution
AN - SCOPUS:85119988217
T3 - Proceedings of the ASME Design Engineering Technical Conference
BT - 47th Design Automation Conference (DAC)
PB - American Society of Mechanical Engineers (ASME)
T2 - 47th Design Automation Conference, DAC 2021, Held as Part of the ASME 2021 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, IDETC-CIE 2021
Y2 - 17 August 2021 through 19 August 2021
ER -