# Mining customer product reviews for product development: A summarization process

arXiv: https://arxiv.org/abs/2001.04200 · HTML: https://mlsanity.com/paper/2001.04200/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 4 of 4)

- [Can Machine Learning Tools Support the Identification of Sustainable Design Leads From Product Reviews? Opportunities and Challenges](https://mlsanity.com/paper/2112.09391/) (2021)
- [Can Online Customer Reviews Help Design More Sustainable Products? A Preliminary Study on Amazon Climate Pledge Friendly Products](https://mlsanity.com/paper/2202.07463/) (2021)
- [An explainable machine learning-based approach for analyzing customers' online data to identify the importance of product attributes](https://mlsanity.com/paper/2402.05949/) (2024)
- [Large Language Models for Token-Efficient and Semantic-Preserving Opinion Summarization](https://mlsanity.com/paper/2607.10825/) (2026)

## Abstract (arXiv)

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. Previous methods were mainly focused on product features. However, reviewers express their preference not only on product features. In this paper, based on an extensive literature review in design science, the authors propose a summarization model containing multiples aspects of user preference, such as product affordances, emotions, usage conditions. Meanwhile, the linguistic patterns describing these aspects of preference are discovered and drafted as annotation guidelines. A case study demonstrates that with the proposed model and the annotation guidelines, human annotators can structure the online reviews with high inter-agreement. As high inter-agreement human annotation results are essential for automatizing the online review summarization process with the natural language processing, this study provides materials for the future study of automatization.