Mining customer product reviews for product development: A summarization process
This work addresses the need for more comprehensive product development insights from customer reviews, though it is incremental as it builds on existing summarization methods by adding new aspects.
This research tackled the problem of extracting structured customer preferences from online reviews to guide product development by proposing a summarization model that includes aspects like affordances, emotions, and usage conditions, beyond just product features, and demonstrated high inter-agreement among human annotators in a case study.
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.