## 2 Related Work
### Review-Based IPA
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Study** & **Type** & **Importance Measure** & **Performance Measure** & **Case Product** \\ \hline [PERSON] et al. [27] & Static & Appearance & \% of positive reviews & Hotel reservation app \\ [PERSON] and [PERSON] [28] & Static & Appearance & Average rating & Airline service \\ [PERSON] et al. [29] & Static & Appearance & Sentiment score (1) & Smartphone \\ [PERSON] et al. [30] & Static & Appearance & \% of positive reviews, & \\  & & & & Satisfaction score (2) & Hotel service \\ [PERSON] and [PERSON] [31] & Static & Significance & Sentiment score & Hotel service \\ [PERSON] et al. [32] & Static & Significance & Satisfaction score & Automobile \\ [PERSON] and [PERSON] [25] & Static & Significance & Sentiment score & Smartphone \\ [PERSON] et al. [33] & Static & Appearance & Sentiment score & Hotel service \\ [PERSON] et al. [34] & Dynamic & Appearance & Average rating & Landscape of urban park \\ [PERSON] et al. [26] & Dynamic & Significance & Sentiment score & Smartphone \\ [PERSON] et al. [35] & Dynamic & Significance & Sentiment score & Hotel service \\ This article & Dynamic & Appearance & Complaint interval & Hotel service \\ \hline \hline \end{tabular}  
* Sentiment score. % of positive reviews – % of negative reviews;
* Satisfaction score: number of positive reviews/(number of positive reviews + number of negative reviews).  
\end{table} 
Table 2: Summary of studies on review-based IPA. Figure 1: Importance-performance analysis (adapted and redrawn from [13]). [PERSON] et al. [29] conducted an IPA based on LDA and deep learning-based sentiment analysis. They first extracted important topics using LDA and then performed sentiment analysis to obtain the performance scores. [PERSON] et al. [33] conducted a review-based IPA using online hotel reviews to explore the strengths and weaknesses of hotels. They extracted the importance of attributes using term frequency-inverse document frequency (TF-IDF) and calculated performance based on sentiment scores. The results were compared across two- to five-star hotels. [PERSON] et al. [34] presented a review-based IPA using visitors' online reviews for urban parks in Beijing, evaluating the most important attributes and identifying parks in need of immediate renewal. Additionally, their findings revealed multiple temporal changes by season. [PERSON] and [PERSON] [31] utilized sentiment analysis and review-based IPA to analyze online hotel reviews. They determined the importance of each attribute by performing a partial correlation analysis between the star rating of the review and the sentiment scores of the attributes. [PERSON] et al. [32] proposed a three-dimensional IPA that divides the importance dimension into two perspectives: positive importance (i.e., weight of positive sentiment on the overall product rating) and negative importance. [PERSON] and [PERSON] [25] employed a deep neural network to determine the importance of smartphone attributes. To obtain importance estimates with lower variance, they combined estimates from multiple optimal neural networks. Similar to conventional survey-based IPAs, most review-based IPAs have been conducted in a static manner, analyzing data all at once without considering the time factor. However, such static IPAs are insufficient for understanding how customer needs change over time. Thus, several recent studies [26; 35] have suggested DIPA, which analyzes changes in the attribute importance and performance levels over time by applying IPA repeatedly to multiple time-series datasets. DIPA can help establish product improvement strategies that consider trends in the market. Focusing on the value of DIPA, the present study proposes a combination of a review-based control chart and a DIPA. Here, DIPA is introduced as a new tool for conducting IPA with review-based control charts. It enables a comparative analysis of multiple attributes from the perspective of complaint analysis and assists in prioritizing attributes for quality improvement.