## 5 Discussion  
The BLCC combined with DIPA is a novel tool for monitoring and analyzing customer complaints through online customer reviews. It assists in observing how customer opinions change over time at the attribute level. Further, it enables us to grasp the average levels of attribute importance and performance in a certain period and to identify whether any special-cause variations have occurred; if special-cause variations exist, the tool also indicates the type(s) of variations. Using the new tool, one can easily identify any significant changes to the level and content of customer complaints. It is also possible to discern whether the changes are a natural event, a temporal aberration, or a gradual trend so that appropriate plans for quality management can be established. Figure 8: DIPA for Hotel A. The numbers 1–3 above each data plot refer to the stage of the corresponding control chart. \begin{table}
\begin{tabular}{c c c c c c c c} \hline  & **FA** & **BC** & **RC** & **DF** & **SS** & **FC** & **LS** \\ \hline Stage 1 & 0.8659 & 0.8709 & 0.8463 & 0.9716 & 0.9458 & 0.8388 & 0.9527 \\ Stage 2 & 0.8420 & 0.8721 & 0.8286 & 0.9094 & 0.8996 & 0.8752 & 0.9386 \\ Stage 3 (part) & 0.6257 & 0.7786 & 0.8340 & 0.7866 & 0.9164 & 0.8329 & 0.9411 \\ \hline \end{tabular}
\end{table} 
Table 10: Performance index values used in the DIPA for Hotel A. Although several review-based control charts exist, the proposed method differs from existing ones in several ways. First, the proposed method converts a corpus of reviews into two types of control charts, separately visualizing attribute importance and performance. Such a separation of importance and performance facilitates highly accurate apples-to-apples comparison of quality levels among multiple attributes. This allows for identifying the main reasons for customer complaints and highlighting attributes in urgent need for improvement. Resources can be allocated accordingly for improving the attributes with high importance but low performance. Second, the proposed approach is generally applicable and less susceptible to the uncontrollable and inconsistent nature of online customer reviews. This means that the current method aligns well with the natural characteristics of online customer reviews. To create a \(p\)-chart, the number of reviews must meet the minimum subgroup size requirements, which increase as the value of \(p\) decreases (i.e., \(3/p_{it}\) and \(3/(1-p_{it})\)). If a review subgroup fails to meet these requirements, it is excluded from the chart, resulting in unavoidable data loss. Table 11 compares the data loss of the proposed method with that of previous review-based \(p\)-charts. Previous methods monitor the fraction of complaints for each attribute directly, leading to small \(p\) values and a high probability of data loss. In contrast, the proposed method considers attribute importance and performance separately, with the \(p\)-chart at the top level monitoring the fraction of attribute appearance, which is naturally greater than the fraction of complaints. Consequently, the chance of data loss is reduced. Moreover, even in cases where there is insufficient data to generate \(p\)-charts at the top level, g-charts at the bottom level remain applicable. This implies that performance monitoring is possible in most situations, regardless of the amount of data available. In this case study, an average of 43% of the data was lost when using previous methods due to inadequate review subgroups. In contrast, the proposed method was more effective, with only 15% of data lost at the top level and no data loss at the bottom level. Third, the proposed method is customizable and expandible depending on the situation. The UCL and LCL can be adjusted (narrowed or widened) according to the user's requirements; additional lines that show the limits for specifications can be used to monitor whether the current quality level is satisfiable.