## 6 Conclusions  
This article proposes the use of BLCCs with DIPA as a novel technique for monitoring customer complaints utilizing online customer reviews. The primary objective of the BLCCs is to extract the importance and performance of attributes separately from customer reviews and to plot them in a two-level control chart. The BLCCs are review-based control charts that are less susceptible to the natural characteristics of online reviews and have a broader range of applications than existing methods. Due to their applicability even with a relatively small number of reviews, they can be employed by small private shops as well as large enterprises. The DIPA is introduced as a new tool for conducting IPA based on BLCCs. By providing a comprehensive view of multiple BLCCs through a single chart, the DIPA reveals changes in the average levels of importance and performance of each attribute over time. This information is valuable in prioritizing multiple attributes for improvement and establishing quality management strategies. The main contribution of the proposed method is that it provides an integrated analysis framework linking statistical control charts with customer review analytics. The techniques utilized for extracting attributes and conducting sentiment analysis were not addressed in the present study, as the advancement of sentiment analysis techniques is left beyond the scope of this article. However, their performance is crucial for the success of review-based  
Figure 9: DIPA for Hotel B (four-star hotel in Seoul). The numbers 1-3 above each data plot refer to the stage of the corresponding control chart. control charts, and it is worth noting that the proposed approach can perform even better with the availability of more advanced natural language processing (NLP) techniques. For example, the present lexicon-based approach for Korean reviews could be replaced with a deep learning-based or a machine learning-based approach if proven more effective. The inclusion of the intensity or strength of sentiments is also worth considering. Another intriguing research topic for future studies is the reflection of linguistic diversity and the detection of emerging attributes. To adapt the proposed framework to different languages, future research should incorporate sentiment analysis for multiple languages. Furthermore, a language's lexicon is continuously evolving, with new words and meanings being added over time. As such, updating and upgrading attribute lexicons is crucial for the success of the proposed method, and more research is needed in this area. One limitation of control charts is that they are only able to classify the types of variations, without providing information on the underlying causes or reasons. Therefore, it is impossible to determine the specific factors contributing to changes in appearance frequency or complaint levels. To identify these root causes, a more detailed investigation of corresponding reviews is necessary. In this regard, combining BLCCs with topic modeling could be a promising avenue for future research. Further research is necessary to incorporate competitors into the analysis to achieve a more accurate diagnosis of attribute importance and performance. Comparing products with their competitors in the market is recommended. As a potential method for future studies, it is suggested to establish the baseline values of DIPA at the market level by considering competitors' attribute importance and performance. This can help understand the company's competitiveness in the market and identify opportunities for differentiation from competitors, enabling more informed quality improvement decisions. Additionally, future research can consider extending the current BLCCs, which focus on a single attribute of a single product, to a multivariate control chart. This would provide opportunities for large-scale analyses and a broader range of applications, including quality management at the franchise, brand, and industry levels. Conceptualization, S.K. and M.K. ; methodology, S.K. and M.K.