## 1 Introduction  
In the current dynamic and highly competitive business environment, companies are compelled to monitor the voice of customers (VOC) and consistently improve their products, encompassing both tangible goods and intangible services. Online customer reviews are a valuable source for the VOC, as they reveal whether customers are satisfied with a product and the reasons for their satisfaction or dissatisfaction [1; 2]. Negative feedback or complaints in reviews are particularly important as they highlight existing or potential quality issues, which can furnish a product's reputation and adversely influence potential customers' purchasing decisions [3; 4; 5]. Therefore, to achieve customer satisfaction and facilitate future sales, it is crucial for companies to closely monitor the prevalence of negative reviews, identify the root causes of complaints, and develop appropriate strategies to enhance product quality and customer satisfaction [1; 6; 7]. Performing attribute-level sentiment analysis of online customer reviews can offer a productive and practical approach to monitor customer complaints. Sentiment analysis extracts customer opinions for an attribute from written text and identifies whether their sentiments for the attribute are positive, negative, or mixed [8]. By using sentiment analysis, companies can count the number of negative reviews for each attribute, which serves as a measure of the quality of specific attributes. This approach helps identify quality concerns and prioritize attributes that require improvement [6; 9]. In this context, the integration of sentiment analysis and conventional control charts into a review-based control chart has emerged as a novel approach for analyzing customer complaints [3; 10]. A review-based control chart first utilizes sentiment analysis to measure customers' opinions on specific attributes and then plots the results, typically the count or proportion of negative reviews over a given period, in chronological order using a control chart. Review-based control charts enable time-series analysis of complaints and facilitate monitoring of the average level of complaints and their fluctuations over time, which can signal any need for quality improvement. Given their efficiency and effectiveness in utilizing automated analytics, review-based control charts are increasingly attracting attention as alternatives or complements to conventional survey-based monitoring tools, such as customer satisfaction surveys and SERVQUAL. Starting with the study by [PERSON] [11], previous studies have demonstrated the potential and value of review-based control charts. However, as pointed out in [PERSON] et al. [12], existing approaches have fundamental issues that may prevent them from fitting the natural characteristics of online customer reviews, namely, the inconsistency and uncontrollability of the number and content of reviews. In real-world cases, there is a chance that an extremely small number of reviews are posted. Moreover, customers usually mention only the attributes that they consider important or those they consider an interesting point differentiating the reviewed product from competitors. Accordingly, the number of reviews with respect to an attribute is likely to be smaller than the total number of reviews, indicating an increased probability of failure to meet the minimum sample size requirement for a valid control chart. The interpretation of control charts also raises concerns. As customers tend to mention only the attributes that they think are important, the complaint rate (charting value) of an attribute tends to increase with the attribute's importance, regardless of its actual performance. A smaller value does not necessarily indicate fewer complaints or better performance; it can also indicate a lower attribute importance. This implies that the current charting value does not support an apples-to-apples comparison of attribute performance.