  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.
