Customer Complaint Analysis via Review-Based Control Charts and Dynamic Importance–Performance Analysis

Review-based control charts that integrate sentiment analysis with traditional are emerging as effective tools for analyzing customer complaints. However, existing approaches face significant challenges due to their lack of alignment the natural characteristics online reviews, such limited review volumes or imbalanced mentions attributes. To address these challenges, which commonly encountered in real-world applications, this study proposes a novel framework review-based complaint combines bi-level chart dynamic importance–performance analysis. The proposed method converts unstructured reviews into set statistical charts, analyzes how average and variation attribute importance performance change over time, identifies whether changes event, temporal aberration, gradual trend. This allows conducting apples-to-apples quality comparison among multiple attributes establishing appropriate improvement strategies real-time proactive manners. demonstrate applicability effectiveness method, an empirical case using hotel is presented.