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Review-based control charts that combine online review mining with traditional control charts are emerging as a new tool for quality monitoring. Although previous studies have provided successful examples of the review-based control chart, the natural characteristics of user-generated reviews and their impact on the chart have received less attention so far. After briefly surveying existing approaches, this paper presents a case study of two hotels in Korea to demonstrate the potential issues and challenges faced by review-based control charts because of the characteristics of online customer reviews. The results show that a small, inconsistent number of reviews and their uncontrollable contents poses the main challenge for the review-based control chart. Future research directions are discussed based on the results.
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ICIC Express Letters
Part B: Applications ICIC International c
2021 ISSN 2185-2766
Volume 12, Number 8, August 2021 pp. 707–714
Suah Kim, Sohyun Park and Minjung Kwak
Department of Industrial and Information Systems Engineering
Soongsil University
369 Sangdo-ro, Dongjak-gu, Seoul 06978, Korea
{suak210; sohyunpark }; Corresponding author:
Received December 2020; accepted February 2021
Abstract. Review-based control charts that combine online review mining with tradi-
tional control charts are emerging as a new tool for quality monitoring. Although previous
studies have provided successful examples of the review-based control chart, the natural
characteristics of user-generated reviews and their impact on the chart have received less
attention so far. After briefly surveying existing approaches, this paper presents a case
study of two hotels in Korea to demonstrate the potential issues and challenges faced
by review-based control charts because of the characteristics of online customer reviews.
The results show that a small, inconsistent number of reviews and their uncontrollable
contents poses the main challenge for the review-based control chart. Future research
directions are discussed based on the results.
Keywords: Service quality, Statistical process control, Control chart, Online review,
Opinion mining, Sentiment analysis, Text mining, Hospitality management
1. Introduction. In today’s competitive environment, the service quality perceived by
customers and the resulting customer satisfaction are critical success factors of a firm [1].
Therefore, continuous efforts have been made in academia and industries to develop more
advanced tools for service monitoring and management. Online review mining is one such
tool that is receiving increasing interest in various service industries. It analyzes online
customer reviews using natural language processing and extracts peoples’ sentiments and
opinions from written text [2,3]. Through online review mining, firms can better un-
derstand customer requirements, identify the service attributes that are important for
customer satisfaction, and determine whether their customers are satisfied with certain
service attributes [3-6].
Combining online review mining with traditional control charts is a recent attempt to
visualize and monitor the perceived service quality [7-11]. In this technique, online review
mining first measures the service quality based on the customers’ sentiment toward various
service attributes; then, a control chart displays the results (usually the number or the
proportion of complaints in a given period) in time order. Such review-based control
charts provide valuable information about the average service quality and variation in its
level, help monitor the service quality over time, and highlight any need for improvement.
They can complement traditional survey-based monitoring tools (e.g., SERVQUAL) that
require considerable time and money [9].
Starting with Lo [8], previous studies have suggested various versions of review-based
control charts adopting different combinations of review mining and control chart tech-
niques [8-11]. They have shown that the review-based control chart can be a promising
solution for service monitoring. However, the natural characteristics of user-generated
reviews and their impact on the application and operation of control charts have received
DOI: 10.24507/icicelb.12.08.707
less attention so far. This study aims to investigate and highlight the potential issues
regarding the review characteristics. After a brief survey of existing review-based con-
trol charts, this paper presents an empirical case study of two hotels in Seoul, Korea to
demonstrate the problems that may arise when employing the review-based control chart
in the real world. The case study illustrates that a small, inconsistent number of reviews
and their uncontrollable contents pose the main challenge for review-based control charts.
The rest of the paper is organized as follows. Section 2 reviews recent literature. Section
3 presents the case study and demonstrates the potential and limitations of existing
approaches. Section 4 concludes the paper with future research directions.
2. Literature Review. A control chart is a statistical process control tool to detect
whether a manufacturing or business process is in a state of control. Control charts have
been widely used in a variety of fields for quality monitoring, and many studies have been
conducted to improve control charts and their applications (e.g., [12,13]). Recently, several
studies have proposed review-based control charts that create a control chart (usually a
P- or an exponentially weighted moving average (EWMA) chart) based on the results
of online review mining. Table 1 summarizes the recent studies on review-based control
charts including this paper.
Table 1. Summary of the research on review-based control chart
Ref. Data
[8] Customer
(as a whole) Week SVM No Rate of
complaints P-chart
[9] Complaints Topic
(attribute) Week LSA No Eigenvector
component value
[10] Complaints Topic Week LSA No Rate of
complaints P-chart
[11] Reviews Service Day SRJST Positive
paper Reviews Topic Month Lexicon-based
Rate of
Lo [8], who first attempted a review-based control chart, classified customer messages
into complaints and non-complaints using support vector machine (SVM) and plotted a
P-chart displaying the rate of complaints. The P-charts issue a warning signal if the rate
of complaints exceeds the usual level.
Ashton et al. [9,10] combined the EWMA and P-charts with the latent semantic analysis
(LSA) technique. They collected complaints from customers who requested a refund and
identified several complaint topics using LSA. For each topic, they suggested an EWMA
chart plotting the topic’s eigenvector component value [9] or a P-chart displaying the
referring proportion of the topic [10]. An underlying assumption of these works is that
the customer comments are all negative. Sentiment classification was not incorporated.
Liang and Wang [11] proposed the sequential reverse joint sentiment-topic (SRJST)
chart, a new control chart that can detect shifts in topic-sentiment combinations in a
corpus (a group of reviews). A review mining method SRJST was proposed to estimate the
joint topic-sentiment distribution per day. The charting statistics monitor the estimated
daily joint distribution and assess if it is similar to the expected reference distribution.
Previous studies have shown that the review-based control chart can be an effective
method for service monitoring. However, they have focused on proposing charting meth-
ods and paid relatively less attention to determining the effect of the online reviews’ nat-
ural characteristics on the application of review-based control charts. This paper presents
a case study of two hotels in Korea to demonstrate the potential issues and challenges
due to the nature of online customer reviews. With the case study, this paper details
the achievements and limitations of existing approaches and suggests future research di-
3. Case Illustration. This section presents an empirical study of two hotels in Seoul,
Korea: Hotels A and B. Online customer reviews written in Korean from January 2018
to April 2020 were crawled from the website of and used for the case
3.1. Methodology. Creating a review-based control chart is a two-step approach: 1)
mining online customer reviews, and 2) plotting a control chart. Figure 1 shows the
overall flow of the approach. In this study, review mining was conducted following the
lexicon-based method from the authors’ prior research [4,5]. For a predefined set of service
attributes (furniture and appliances, bathroom condition, room condition, dining, service,
facilities, conveniences, and surroundings; eight attributes in total), the method identifies
whether a review refers to a certain attribute, and if so, what is the sentiment for the
Figure 1. Creating a review-based control chart at an attribute level
The results from the review mining are visualized using P- and EWMA charts. A
P-chart is a control chart for the fraction nonconforming. In this study, the fraction non-
conforming represents the rate of complaints for a given service attribute in a sample. In
this study, the fraction nonconforming is calculated using Equation (1). Here, pni denotes
the rate of complaints for attribute nin the month i. The sample size (denominator) is
the number of reviews in a month and thus, varies from time to time.
pni =# of reviews with negative sentiments for attribute nin month i
# of reviews posted in month i(1)
The EWMA chart is a time-weighted control chart that considers not only the current
sample observation but also the entire previous samples. It charts the EWMA of all
sample observations. A constant λ(0 < λ 1) determines the weight given to each
point. The EWMA chart is more effective than the P-chart in detecting small process
shifts. In this study, the charting value xn,i denotes the EWMA of pnis, and is calculated
as shown in Equation (2).
xn,i =λpni + (1 λ)xn,i1(2)
3.2. Example of successful application. Hotel A illustrates how review-based control
charts can effectively assist in service monitoring. Figure 2 shows some of the results
from Hotel A. A total of 971 reviews were collected and classified by month. The average
number of reviews per month was 34.
(a) P-chart (room condition) (b) EWMA chart (room condition)
Figure 2. Review-based control charts for monitoring the service quality
of Hotel A
Figures 2(a) and 2(b) show the P- and EWMA charts, respectively, for the room condi-
tion of Hotel A. They provide an effective overview of the service quality at the attribute
level. The average performance and its trend over time are easily observable. There was
one out-of-control point in Figure 2(a) in January 2018, and there were four out-of-control
points in Figure 2(b) from January to April 2018. The results imply that there might
have been a special cause for the negative feedback from the hotel guests on the room
condition. Hotel managers can conduct a detailed analysis to determine the reasons for
the feedback and take necessary actions. The detailed analysis of the months showed that
most of the complaints had come from the “oldness” of the room.
3.3. Example of difficulties. In contrast to the example of Hotel A, the case study of
Hotel B reveals some difficulties in applying review-based control charts that are especially
due to the uncontrollable quantity and quality of online customer reviews. Figure 3 shows
some of the case study results of Hotel B: the review count and the P-charts.
Figure 3(a) displays the total number of reviews per month (black solid line) and the
number of reviews mentioning a particular attribute per month (dotted lines). Here, lines
for three service attributes, i.e., room condition (RC), service, and facility, were illustrated
as an example. Figure 3(a) exhibits the natural characteristics of online customer reviews,
i.e., inconsistent quantity and quality. The number of reviews greatly varies, and more
importantly, there is a chance that an extremely small number of reviews are posted. For
instance, Hotel B has one review in March 2018 and two reviews in April 2020.
In terms of quality, the review content cannot be controlled and customers usually do
not mention every attribute in their reviews. Only the attributes they think important
or worth sharing the information about tend to be referred. Accordingly, the number of
reviews mentioning a particular attribute is likely to decrease from the total number of
reviews, which means an increased probability of only few or zero reviews per month.
Such a small number of reviews complicates the application of control charts in several
aspects. First, the performance of the control limit is not guaranteed under the small
number of reviews. A small sample size increases the upper control limit and the control
charts become less reliable. According to [14], the rule of thumb for the minimum sample
size of the P-chart is at least 0.5 divided by the mean pni, i.e., 4 (RC) to 12 (facilities) in
this case.
(a) Number of reviews (b) P-chart (RC)
(c) P-chart (facilities) (d) P-chart (service)
Figure 3. Review count and the P-chart examples of Hotel B
Second, when the pni is determined by only a few review(s), the relative weight given to
a single review increases in the pni calculation. This can be a serious problem, considering
the possibility that the single review is an aberration (e.g., false review).
Third, even if the absolute difference in the number of reviews is small, the difference
in pni values can be exaggerated, resulting in misconception about the service quality.
Taking the P-chart in Figure 3(c) as an example, the numbers of negative reviews in
March 2018 and April 2020 were 0 (out of 1 review) and 1 (out of 2 reviews), respectively,
and their pni values were 0 and 0.5, respectively. Given those pni values, one may conclude
that the service quality in March 2018 was much better than in April 2020. However,
considering that the actual difference between the two time points was just one negative
review, the appropriateness of the evaluation is questionable.
Fourth, the charting value pni also raises a concern regarding the result interpretation.
Figures 3(b)-3(d) show that the average pnis of room condition, service, and facilities are
0.155, 0.086, and 0.043, respectively. One may conclude that the performance of facilities
is better than that of service or room condition. However, this can be a misjudgment. As
shown in Equation (1), the sample size (i.e., denominator) is the number of entire reviews
in a month, and thus, it is the same for all attributes; but as some attributes are more
important to customers, a negative reference of those (i.e., numerator) tends to increase
naturally in attribute importance. In other words, a smaller pni does not necessarily mean
fewer complaints or better performance. It can also indicate lower attribute importance.
The EWMA chart also has the same issues as the P-chart. A small sample size and
the risk of being affected by aberrations are more critical in the EWMA chart, as one
review can influence other charting values afterward. This means that the entire chart
can be easily swayed by a single review. Figures 4(a) and 4(b) provide an example. Figure
(a) EWMA chart (RC, pmar-18 = 1) (b) EWMA chart (RC, pmar-18 = 0)
Figure 4. EWMA charts for the room condition (RC) of Hotel B
(a) Avg. number of reviews (b) Avg. % of negative reference/reference
Figure 5. The quantity and quality characteristics of the online reviews
in hotel industry
4(a) shows the EWMA chart when the only review in March 2018 was a negative one.
By contrast, Figure 4(b) assumes that the review was a positive one. There is only one
difference, but great differences in control limits and charting values are observable.
Similarly, the EWMA chart needs careful consideration when dealing with missing
values. A rule for substituting the missing values should be chosen carefully as the rule
affects not only a single charting value but also the entire chart and its performance.
One may suspect that Hotel B is a special case experiencing the aforementioned prob-
lems. However, Figure 5 shows that the small, inconsistent number of reviews and uncon-
trollable, unequal reference to the service attributes are not rare in the real world. Figure
5(a) shows the average number of reviews per month for the 3-5 star hotels in Seoul,
Busan, and Jeju, Korea (351 hotels in total). The average number of monthly reviews is
1.94; 85% of the hotels had only two or fewer reviews per month. Figure 5(b) shows the
rate of the reviews mentioning each service attribute, and the rate of negative reviews for
the attribute. The average rate of reference ranges from 18% to 77%; if considering only
the negative reviews, the average rate drops to 3-18%. This implies that the sample size
drops even more at the attribute level, and the aforementioned problems are expected to
be common in other hotels as well.
4. Discussion and Conclusions. Review-based control charts are emerging as a new
tool for service monitoring and improvement. However, applying a traditional control
chart to user-generated reviews in its current form can entail various problems.
In the review-based control chart, it is almost impossible to control the number and
content of the reviews. The fact that not all attributes appear in a review complicates the
problem. The sample size drops at the attribute level, and missing values increase. As the
impact of a single aberration increases and the control limits become questionable, the
risks of false or no alarm increase. One may consider changing the sampling interval (e.g.,
from every month to every quarter) to get enough samples, but a loss of performance is
unavoidable as a process shift cannot be detected on time. Therefore, unless a sufficiently
large amount of good-quality reviews can be collected continuously, the performance and
usefulness of the review-based control charts are likely to face challenges.
The interpretation of a P-chart also raises concerns. The pni values of important at-
tributes tend to be greater than those of other attributes, regardless of their actual per-
formance. Such a difference of pni can lead to a misjudgment of an attribute’s quality.
The review-based control chart requires a new approach that can address the challenge
of uncontrollable characteristics of customer reviews. A modified control chart should be
developed in the future. Adopting alternative quality measures, such as the sentiment
score (intensity of sentiment), the ratio between positive and negative reviews, or the
interval between complaints, can be an option worth considering. Changing the chart
type according to the new measures and setting the control limits in different ways can
also be considered.
Acknowledgment. This work was supported by the National Research Foundation of
Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT, MSIT)
(No. NRF-2019R1F1A1041099).
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... Most of them have proposed reviewbased control charts based on either a p-chart or exponentially weighted moving average (EWMA) chart with a constant time interval (e.g., a week or month). However, as pointed out in the authors' previous work [2], existing approaches have fundamental issues that may prevent them from fitting the natural characteristics of online customer reviews: the inconsistency and uncontrollability of the number and content of reviews. ...
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