Search Results for author: Bernard Yannou

Found 5 papers, 0 papers with code

Can Online Customer Reviews Help Design More Sustainable Products? A Preliminary Study on Amazon Climate Pledge Friendly Products

no code implementations20 Dec 2021 Michael Saidani, Harrison Kim, Nawres Ayadhi, Bernard Yannou

In all, for the six products considered, between 12% and 20% of the reviews mentioned directly or indirectly aspects or attributes that could be exploited to improve the design of these products from a sustainability perspective.

Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges

no code implementations17 Dec 2021 Michael Saidani, Harrison Kim, Bernard Yannou

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.

BIG-bench Machine Learning

How circular economy and industrial ecology concepts are intertwined? A bibliometric and text mining analysis

no code implementations2 Jul 2020 Michael Saidani, Bernard Yannou, Yann Leroy, François Cluzel, Harrison Kim

Combining new insights from both bibliometric and text mining analyses, with prior relevant research conversations on circular economy (CE) and industrial ecology (IE), this paper aims to clarify the recent development trends and relations between these concepts, including their representations and applications.

Mining customer product reviews for product development: A summarization process

no code implementations13 Jan 2020 Tianjun Hou, Bernard Yannou, Yann Leroy, Emilie Poirson

A case study demonstrates that with the proposed model and the annotation guidelines, human annotators can structure the online reviews with high inter-agreement.

Mining Changes in User Expectation Over Time From Online Reviews

no code implementations13 Jan 2020 Tianjun Hou, Bernard Yannou, Yann Leroy, Emilie Poirson

Finally, changes of user expectation can be found by applying the conjoint analysis on the online reviews posted for two successive generations of products.

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