Uncovering Sustainability Insights from Amazon’s Eco-Friendly Product Reviews for Design Optimization

Muhammad Rifqi Maarif (), Muhammad Syafrudin and Norma Latif Fitriyani ()
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Muhammad Rifqi Maarif: Department of Industrial Engineering, Tidar University, Magelang 56116, Indonesia
Muhammad Syafrudin: Department of Artificial Intelligence, Sejong University, Seoul 05006, Republic of Korea
Norma Latif Fitriyani: Department of Data Science, Sejong University, Seoul 05006, Republic of Korea

Sustainability, 2023, vol. 16, issue 1, 1-23

Abstract: This research investigates consumer reviews of eco-friendly products on Amazon to uncover valuable sustainability insights that can inform design optimization. Using natural language processing (NLP) techniques, including sentiment analysis, key terms extraction, and topic modeling, this research reveals diverse perspectives related to sustainability aspects in eco-friendly products. Innovatively, we integrate the NLP approach with correspondence analysis (CA) to understand consumer sentiments and preferences related to sustainability aspects. Leveraging CA, we visualize the interplay between eco-friendly product features and consumer sentiments, revealing underlying relationships and patterns. The CA biplot showcases the alignment of specific sustainability attributes with consumer satisfaction, highlighting which sustainability aspects hold greater influence over overall product ratings. As sustainability becomes an increasingly crucial aspect of consumer choices, our paper emphasizes the significance of a multidimensional approach that embraces both qualitative and quantitative insights. By blending CA with consumer reviews, we equip designers and stakeholders with an innovative and comprehensive toolkit to enhance sustainable design practices, paving the way for more informed and effective product development strategies in the realm of eco-friendliness.

Keywords: sustainable product design; eco-friendly product; consumer review; natural language processing; correspondence analysis (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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