Mining Customer Satisfaction from Online Reviews: An Explainable Kano-Based Framework for Product Improvement

Improving customer satisfaction (CS) and gaining competitive advantages are central goals of product improvement, both of which rely on accurate classification of product attributes. Online reviews on e-commerce platforms provide firms with abundant customer feedback, but accurately classifying and prioritizing product attributes remains challenging. To address this issue, we propose an interpretable Kano model. In this method, the Biterm Topic Model (BTM) is first used to identify product attributes from reviews. Then, the Enhanced BERT Model with Attribute-Aware and Convolutional Mechanisms (BERT-A-Conv) is employed to classify the sentiment categories of these attributes. Given the critical role of neutral sentiment, it is incorporated into the Light Gradient Boosting Machine (LightGBM) model to quantify the impact of AS on CS. The Shapley additive explanations (SHAP) method is then adopted to construct the marginal contribution difference (MCD) between adjacent categories, which this study uses to classify product attributes into five Kano categories. On this basis, we calculate the attribute improvement priority score (AIPS) by combining each attribute’s MCD and improvement potential, thereby offering firms a systematic analytical framework to support product iteration and improvement. A case study on smartwatches demonstrates the applicability and feasibility of the proposed method.