Using neutral sentiment reviews to improve customer requirement identification and product design strategies

Abstract

A clear understanding of customer needs is key to the success of product design strategies. Traditional methods of understanding customer needs rely on costly marketing surveys and have difficulties accurately capturing new customer requirements in a fast-evolving market. Online reviews with positive and negative sentiments are commonly used as effective sources for mining customer requirements. Previous studies related to product design strategies often overlook neutral sentiment when analyzing online reviews for product design improvement. In this study, we propose a customer requirement identification framework that identifies the product attributes reflecting customer needs from online reviews, considering three types of sentiment polarities: positive, negative, and neutral sentiment. We categorize the identified customer needs into five product attribute categories that help form product design strategies using the Kano model. Evaluations using two review datasets for laptops and smartphones show that the consideration of neutral reviews caused the majority of the product attributes to be categorized differently by the proposed method. Furthermore, the product categorization obtained from our method achieved a better agreement with domain experts and consumers than that produced by the baseline Kano model, not considering neutral sentiment. We further established evidence that the product design informed by our product categorization results achieved better customer satisfaction than those generated from the baseline Kano model and the questionnaire-based Kano model.