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

• URL: http://arxiv.org/abs/2202.07463v1
• Date: Mon, 20 Dec 2021 08:57:37 GMT
• Title: Can Online Customer Reviews Help Design More Sustainable Products? A Preliminary Study on Amazon Climate Pledge Friendly Products
• Authors: Michael Saidani (LGI), Harrison Kim, Nawres Ayadhi (LGI), Bernard Yannou (LGI)
• Abstract summary: This paper investigates and analyzes Amazon product reviews to bring new light on the following question: What sustainable design insights can be identified or interpreted from online product reviews?'' The top 100 reviews, evenly distributed by star ratings, for three product categories are collected, manually annotated, analyzed and interpreted. 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Online product reviews are a valuable resource for product developers to improve the design of their products. Yet, the potential value of customer feedback to improve the sustainability performance of products is still to be exploited. The present paper investigates and analyzes Amazon product reviews to bring new light on the following question: ``What sustainable design insights can be identified or interpreted from online product reviews?''. To do so, the top 100 reviews, evenly distributed by star ratings, for three product categories (laptop, printer, cable) are collected, manually annotated, analyzed and interpreted. For each product category, the reviews of two similar products (one with environmental certification and one standard version) are compared and combined to come up with sustainable design solutions. 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. Concrete examples of sustainable design leads that could be elicited from product reviews are given and discussed. As such, this contribution provides a baseline for future work willing to automate this process to gain further insights from online product reviews. Notably, the deployment of machine learning tools and the use of natural language processing techniques to do so are discussed as promising lines for future research.

Related papers

• A Literature Review of Literature Reviews in Pattern Analysis and Machine Intelligence [58.6354685593418]
  This paper proposes several article-level, field-normalized, and large language model-empowered bibliometric indicators to evaluate reviews. The newly emerging AI-generated literature reviews are also appraised. This work offers insights into the current challenges of literature reviews and envisions future directions for their development.
  arXiv  Detail & Related papers  (2024-02-20T11:28:50Z)
• Language Models: A Guide for the Perplexed [51.88841610098437]
  This tutorial aims to help narrow the gap between those who study language models and those who are intrigued and want to learn more. We offer a scientific viewpoint that focuses on questions amenable to study through experimentation. We situate language models as they are today in the context of the research that led to their development.
  arXiv  Detail & Related papers  (2023-11-29T01:19:02Z)
• ForeSeer: Product Aspect Forecasting Using Temporal Graph Embedding [25.421723193835895]
  Foreseer offers a novel framework for review forecasting by effectively integrating review text, product network, and temporal information. We evaluate ForeSeer on a real-world product review system containing 11,536,382 reviews and 11,000 products over 3 years.
  arXiv  Detail & Related papers  (2023-10-07T16:21:04Z)
• Exploring the Distribution Regularities of User Attention and Sentiment toward Product Aspects in Online Reviews [7.23135508361981]
  This paper explores the distribution regularities of user attention and sentiment toward product aspects from the temporal perspective of online reviews. The empirical results show that a power-law distribution can fit user attention to product aspects, and the reviews posted in short time intervals contain more product aspects.
  arXiv  Detail & Related papers  (2022-09-08T10:23:16Z)
• GreenDB -- A Dataset and Benchmark for Extraction of Sustainability Information of Consumer Goods [58.31888171187044]
  We present GreenDB, a database that collects products from European online shops on a weekly basis. As proxy for the products' sustainability, it relies on sustainability labels, which are evaluated by experts. We present initial results demonstrating that ML models trained with our data can reliably predict the sustainability label of products.
  arXiv  Detail & Related papers  (2022-07-21T19:59:42Z)
• ItemSage: Learning Product Embeddings for Shopping Recommendations at Pinterest [60.841761065439414]
  At Pinterest, we build a single set of product embeddings called ItemSage to provide relevant recommendations in all shopping use cases. This approach has led to significant improvements in engagement and conversion metrics, while reducing both infrastructure and maintenance cost.
  arXiv  Detail & Related papers  (2022-05-24T02:28:58Z)
• Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges [0.0]
  This paper aims to develop an integrated machine learning solution to obtain sustainable design insights from online product reviews automatically. The opportunities and challenges offered by existing frameworks are discussed, illustrated, and positioned along an ad hoc machine learning process.
  arXiv  Detail & Related papers  (2021-12-17T08:53:58Z)
• User-Inspired Posterior Network for Recommendation Reason Generation [53.035224183349385]
  Recommendation reason generation plays a vital role in attracting customers' attention as well as improving user experience. We propose a user-inspired multi-source posterior transformer (MSPT), which induces the model reflecting the users' interests. Experimental results show that our model is superior to traditional generative models.
  arXiv  Detail & Related papers  (2021-02-16T02:08:52Z)
• Can We Automate Scientific Reviewing? [89.50052670307434]
  We discuss the possibility of using state-of-the-art natural language processing (NLP) models to generate first-pass peer reviews for scientific papers. We collect a dataset of papers in the machine learning domain, annotate them with different aspects of content covered in each review, and train targeted summarization models that take in papers to generate reviews. Comprehensive experimental results show that system-generated reviews tend to touch upon more aspects of the paper than human-written reviews.
  arXiv  Detail & Related papers  (2021-01-30T07:16:53Z)
• Mining customer product reviews for product development: A summarization process [0.7742297876120561]
  This research set out to identify and structure from online reviews the words and expressions related to customers' likes and dislikes to guide product development. The authors propose a summarization model containing multiples aspects of user preference, such as product affordances, emotions, usage conditions. A case study demonstrates that with the proposed model and the annotation guidelines, human annotators can structure the online reviews with high inter-agreement.
  arXiv  Detail & Related papers  (2020-01-13T13:01:14Z)
• Mining Changes in User Expectation Over Time From Online Reviews [0.7742297876120561]
  We propose an approach for capturing changes of user expectation on product affordances based on the online reviews for two generations of products. First, the approach uses a rule-based natural language processing method to automatically identify and structure product affordances from review text. Finally, changes of user expectation can be found by applying the conjoint analysis on the online reviews posted for two successive generations of products.
  arXiv  Detail & Related papers  (2020-01-13T12:57:06Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.