1. Introduction
According to the Singapore Ministry of Foreign Affairs, Singapore, which covers 275 square miles and is inhabited by five million people, a country that is smaller than the state of Rhode Island, is one of the most economically developed countries in Southeast Asia [1,2]. Singapore is also home to one of the world’s busiest transportation hubs, which caters to more than 5000 flights to and from more than 123 countries [3]. Known as the Lion City, tourism has been one of the country’s key pillars in the service and economic sector [4]. The growth in international visitors averages 4.5% to 5.0% annually, and according to UNWTO’s International Tourism Highlights 2020 Edition, Singapore’s international tourism receipts are the highest in Southeast Asia [5,6].
Singapore’s tourism receipts are segregated into five different components: (1) shopping, (2) accommodation, (3) food and beverages, (4) sightseeing, entertainment, and gaming, and (5) other TR components [7]. Of the five components, shopping contributes the most to the total value added (VA) and the country’s GDP. However, despite contributing an average of 4% to the country’s GDP, tourism in Singapore witnessed a shift in spending patterns that resulted in a reduction in these contributions [5]. In addition, although there was a growth in international visitors for more than 20 years, there was a decrease in 2008–2009 due to the financial crisis [8]. However, the slump caused by the COVID-19 pandemic was the most significant in the last two decades.
Due to the rapid spread of the COVID-19 virus in late 2019, Singapore gradually tightened its border controls from early January 2020 until the end of 2021 [9]. Starting in early 2020, Singapore imposed physical-distancing measures to prevent the possibility of transmission, and a quarantine order was enacted [10]. The next significant step Singapore took was the initiation of Phase 2 of the Circuit Breaker on 19 June 2020; in this phase, the tourism sector was allowed to apply to reopen, and eventually, on 28 December 2020, the tourism sector was allowed to increase its operating capacity up to 65% [11]. A new program, called Vaccinated Travelers’ Lane (VTL), allows people to travel to certain countries with certain procedures. Before re-opening the country to international tourists, Singapore created a campaign to reassure locals and encourage them to travel within Singapore. Subsequently, along with the border-tightening measures, the Singapore government also launched a nationwide campaign, called SG Clean, in February 2020 [12]. In this campaign, the Singapore government used the SG Clean certification, which is the national mark of excellence for environmental public hygiene and was created to rally businesses and the public to uphold food sanitation standards and hygiene practices [13]. The web page of SG Passion Made Possible was used as part of a rebranding campaign aiming to drive visitors to visit Singapore while upholding personal hygiene within the SG Clean standards [14].
The research regarding SG Clean is still limited. However, a previous study mentioning SG Clean was focused on dengue prevention. The major focus of the dengue campaign was on promoting public cleanliness, such as maintaining clean premises and preventing littering, which eliminates mosquito breeding habitats and reduces the spread of dengue in addition to COVID-19 [15]. Prior research focusing on the Marina Bay area in Singapore, a key site of post-independence Singaporean urbanism, examined the Marina Bay area to determine how dimensional urban development has been combined with governance practices to produce and extract new territory, showing that the Marina Bay area has become one of Singapore’s most important areas [16]. Staying in this integrated resort is considered a positive and memorable activity [17]. A number of studies have used text mining techniques, such as content analysis, frequency analysis, text-link analysis, and latent semantic analysis, to analyze customer satisfaction with hotel products and services [18]. A previous study also discussed customer perceptions, including satisfaction and dissatisfaction, based on the premise that positive reviews indicate satisfaction and negative reviews indicate dissatisfaction [19]. A more recent study described how the linguistic style in textual reviews affects customer satisfaction [18]. The attributes utilized in this study were sentiment polarity, subjectivity, readability, diversity and word length [20].
Singapore is stepping into a transition towards a new normal; consequently, the tourism and hotel industry needs to be prepared. Based on a review of previous studies, there is limited research utilizing the linguistic styles of customer textual reviews in Singapore SG-Clean-certified hotels. Thus, the main objective of this research is to determine the key attributes affecting customer satisfaction post-COVID-19 in luxury hotels in Singapore’s Marina Bay area by using text mining. In order to achieve these objectives, this study adopted text mining analysis to uncover customer experiences based on online reviews and regression analysis to uncover the main attributes affecting customer satisfaction.
2. Literature Review
2.1. Singapore’s Marina Bay Area Luxury Hotels
Surrounded by world-class leisure destinations and civic spaces, and a part of Singapore’s Central Business District (CBD), the Marina Bay area first took shape in 1970 [21]. In the 1800s, post-independence, Marina Bay was the point of entry for visitors and immigrants coming into Singapore and, in anticipation of growing the area, 38 hectares of land were reclaimed at the bayfront to create the famous Singapore shore profile [22]. This first major development towards the formation of the Marina Centre was the creation of a key business, convention, and hospitality hub [22]. A home to many architectural masterpieces, the Marina Bay area is also one of the important aspects of Singapore’s territorial reconfiguration. Once the first phase of the construction of the Marina Bay area was completed, a cluster of five high-rise hotels and a convention center with a shopping mall became key features of the Marina Bay area [16].
The Marina Bay area is also a destination for those who appreciate luxury; a park in the sky, an infinity pool above one of the icons of Singapore, and the iconic high-rises that make up the Marina Bay sands are all located in this area [23,24]. The Marina Bay area is also filled with many tourist destinations and activities, such as the Singapore Flyer Ferris Wheel, Supertree Grove in the gardens by the bay, the Esplanade, a theater on the bay, the Red Dot Design Museum, and many more [25]. With the Singapore icon, the Merlion, overlooking the Marina Bay, various activities and sightseeing locations are open for tourist visitation [26].
A previous study examined the Marina Bay area’s luxury hotel branding and customer reviews, which used online surveys based on specific scenarios. In that study, the authors of the reviews described themselves as having worked hard the previous year, earning bonuses that were sufficient to cover the cost of a luxury hotel located near the Marina Bay sands. The scenario in the low-deserving condition stated that the authors put little effort into their work and could only afford to stay in the hotel due to family wealth [27]. Meanwhile, a study that examined the impact of integrated resort branding experiences that used the Marina Bay sands area as a sample showed that brand experiences are critical to the transformation of needs satisfaction into well-being [17].
The Marina Bay area also represents a modern luxury lifestyle, which includes luxury hotels. Previous studies defined the experience of luxury hotels as complex perceptions of individuals towards the hotels themselves [28,29]. However, older studies define it as a mix of the beliefs, ideas, and impression of a customer towards the hotel [30]. The Marina Bay area, which is considered a global banking capital, has also been a tourist hub for the last three decades [25]. However, due to the COVID-19 pandemic, there was a significant decline in the tourism industry [31]. Especially, in the tourist hub in Singapore’s Marina Bay area. A report from the Singapore Tourism Board, which can be seen in Table 1, indicates that COVID-19 caused a significant shift in the luxury hotel market in Singapore, as the occupancy rates of luxury hotels increased to 91.1% in the third quarter (Q3) of 2019 and declined to 46.7% by the third quarter of 2020; however, there was a small increase in 2021 Q3 of 52% [32,33]. Even though there was an increase in the room occupancy rate, there was still a drop in the average room rate of luxury hotels in Singapore from SGD 471 in Q3 2019 to SGD 291.7 in Q3 of 2020 and SGD 295 in 2021 Q3 [32,34]. The drop observed from 2019 to 2020 was due to the travel disruption caused by the COVID-19 pandemic; the number of international visitors fell as much as 85.7% in 2020 [35].
2.2. Customer Textual Reviews
Customer textual reviews are defined as per-generated product evaluations that are posted on the internet through company or third-party websites [33]. The most common reason why customers write reviews revolves around customers’ satisfaction in light of their expectations, their desire for better services in the future, and their desire for social support [36,37]. A number of previous studies utilized online reviews to provide insight into various industries. For example, one study used online reviews to investigate the key attributes contributing to customer satisfaction in hotels, and a more recent study adopted online reviews to predict airline customers’ recommendations [38,39]. A further study proved that customer textual reviews influence hotel reputations and potential customers’ purchase decisions [40]. In addition, another researcher described how hotel managers can forecast and review hotel performances based on online reviews [41].
Due to the extreme growth in popularity of online reviews, potential travelers take into account the online reviews they read prior to making decisions [33]. It was mentioned in the previous study that managing online reviews is a marketing strategy that can be used for information generation and revenue generation [36]. Essentially, positive online reviews lead to the dissemination of positive information regarding the hotel and result in positive actions by potential customers in the form of the finalization of bookings, whereas negative reviews discourage bookings [42]. Several studies investigated the attributes that influence tourists by analyzing online reviews and produced a range of similar, if sometimes contradictory, evidence [43]. Consumers of accommodations increasingly seek to use the comments of travel sites to support their decisions, as well as to share their experiences, whether positive or negative [44]. These comments gain in importance when planning the desired travel profile. Furthermore, eWOM is widely disseminated through online reviews and has the potential to influence future customers’ purchase intentions, trust, and demand, as well as hotels’ financial performances [18,45,46]. Furthermore, customer textual reviews can provide additional information about products and services, which in turn can improve current and potential customers’ pre-purchase assessments of these products and services in order to assist them in making better buying decisions [47]. Using online customer reviews, hotels are able to identify the expectations and needs of their customers and improve their products and services accordingly [48].
2.3. Customer Satisfaction Attributes
A previous study defined customer satisfaction as an individual’s positive or negative feelings after the consumption of services or products [49,50]. In this study, customer perceptions include satisfaction and dissatisfaction based on the premise that positive reviews indicate satisfaction and negative reviews indicate dissatisfaction [19]. A number of studies have used text mining techniques, such as content analysis, frequency analysis, text-link analysis, and latent semantic analysis, to analyze customer satisfaction with hotel products and services [18].
An analysis of the relationship between textual hotel reviews and online customer ratings focused on the sentiment polarity of online customer reviews and found that sentiment polarity influences customer ratings [51]. A high correlation was observed between the sentiment scores of titles and contents of online customer reviews and overall hotel ratings using natural language processing, text mining, and sentiment analysis techniques [52]. It was concluded that most of the attribute sentiments captured in the textual reviews were significantly related to the customers’ overall rating [53]. This also supports previous research findings that indicate that overall ratings are the greatest predictors of hotel performance [54].
This study contributes to this research stream by examining the linguistic style of online reviews in order to understand how customers evaluate hotels. As part of our study, we included additional technical variables: diversity, readability, and length [18,51]. Additionally, the most common keywords that were retrieved from Google Travel were also discussed and visualized. Through the analysis of big data, this study sought to predict overall customer satisfaction through various technical variables of customer reviews, which can assist hotel owners in predicting the future performances of their hotels, benchmarking properties, forecasting occupancy rates, and improving corresponding operations amidst the fierce competition among hotels [18,55].
3. Hypotheses Development
3.1. Sentiment Polarity
Customer sentiment includes both negative and positive extreme emotions, including frustration, anger, delight, and excitement [51]. In online reviews, sentiment polarity is determined by the degree to which customers express positive or negative sentiments. Positive sentiment is indicated by higher polarity. It has been demonstrated that positive emotions can enhance the perception of the quality of products and services, which is an antecedent to customer satisfaction, whereas negative emotions are antecedents to customer dissatisfaction [20]. In general, customers tend to evaluate their consumption experiences more positively when they are in a positive emotional state as opposed to when they are in a negative emotional state.
3.2. Readability
Readability refers to the difficulty of understanding the meaning of online reviews. In general, higher readability indicates that readers need a higher level of education and maturity in order to understand the meaning of texts. The linguistic style of a review with a higher readability score usually indicates a higher level of education [20]. Those with higher levels of education are more likely to be critical, which leads to customer dissatisfaction [56]. Customers use more advanced words to describe their experiences in detail when they are dissatisfied with the product and service attributes of hotels and wish to persuade hoteliers and future guests [57].
3.3. Word Length
Customers are more likely to post more words and sentences describing the negative aspects of hotels’ products and services than words describing the positive aspects [57]. The longer the review, the more effort the has customer put into describing the product or service, which is often the case when they experience negative emotions during consumption [20]. When encountering the drawbacks of products and services, customers use more words to convey their frustration, anger, and depression [19].
3.4. Diversity
Customers tend to use a variety of words in order to describe several positive aspects of hotel products and services [58]. Diversity, as used in this study, refers to the redundancy of words in online reviews. The use of fewer redundant words in customer reviews is associated with higher diversity. Customers describe hotel products and services in a variety of ways, which results in a diversity of words in positive reviews [20,58].
3.5. Research Model
According to the literature review above, this study proposes the research model presented in Figure 1. The figure shows four attributes: sentiment polarity, readability, word length, and diversity. The relationship between the four attributes and the overall customer satisfaction is presented through hypotheses one to four.
Sentiment polarity has a positive impact on the overall customer experience in luxury hotels in Singapore’s Marina Bay area.
Readability has a positive impact on the overall customer experience in luxury hotels in Singapore’s Marina Bay area.
Word length has a positive impact on the overall customer experience in luxury hotels in Singapore’s Marina Bay area.
Diversity has a positive impact on the overall customer experience in luxury hotels in Singapore’s Marina Bay area.
4. Methodology
4.1. Variables and Measurements
Online reviews are becoming increasingly important in an era in which the Internet is one of the most important aspects of life [50,59]. Prior to making any purchase decision, perusing product or services’ online reviews on the internet is an essential step [60]. Consequently, the popularity of hotels depends on online reviews posted after customers stay, since a large number of negative reviews can easily undermine a hotel [58]. The dependent variable in this study is customers’ overall satisfaction and the independent and dependent variables are sentiment polarity, diversity, readability, and word length. The operational definition of each variable can be found in Table 2.
4.2. Data Collection
Online reviews are becoming increasingly important in an era when the Internet is one of the most important aspects of life [49,50]. An example of an online review can be seen in Figure 2. Prior to making any purchase decision, going through a product or service’s online reviews on the Internet is one of the essential steps in this era [60]. With this, hotel popularities depend on the online reviews posted as a post purchase action since a large number of bad reviews will be able to take a hotel down easily [58].
This study collected customer reviews from Google Travel, a web application that allows potential travelers to search for accommodation at their trip destination, the best time of year to visit, and many other features [65]. The list in Table 3 features seven hotels on the Singapore Government’s SG Clean List, which are located in the Marina Bay area. The customer ratings were posted during 2020–2022 [66]. For this purpose, SCTM 3.0 (Smart Crawling and Text Mining) was utilized. SCTM 3.0 is a web-crawling and text mining program developed by the Wellness and Tourism Big Data Institute of Kyungsung University [50].
5. Results
5.1. Analysis of Word Frequency
After the text mining, 8441 reviews were sorted and collected from the seven hotels. Table 4 summarizes the frequency of the numerical ratings from 1 to 5. This table can be used as a baseline to evaluate the customer satisfaction levels. During 2020–2022, the reviewers who stayed in the Marina Bay area gave their experiences at their respective hotels an average rating of 4.57 out of 5, and 87% of the reviewers gave their hotel a four or five-star rating. In general, 8% of the customers were not satisfied with their experience, since they gave a rating of 1 or 2.
As a result, the words that appeared in the valid comments collected were ranked by their frequency. Table 5 shows the 70 most frequent words relating to customer satisfaction that were extracted and sorted. The most frequent word was “hotel”, with a frequency of 5257. The words “room” and “service” were in the second and third position, respectively, with a frequency of 4761 and 2949, respectively. In fourth position was “stay”, with a frequency of 2533.
Meanwhile, “staycation” and “clean”, which reflected the timing of this research, which focused on post-pandemic and SG-Clean-certified hotels, were in the twenty-fourth and twenty-eighth position, respectively, with frequencies of 816 and 694, respectively. The main reason that these words were not in the first position, or in the top, is that the data were taken in 2020, when the SG-Clean-certified hotel regulation started, and the tourism in Singapore slowly resumed.
Some guests were unfamiliar with the term “staycation”, which has become more familiar post-pandemic. This is because, after the long initial phase of the pandemic, many people stayed at home and could not travel for leisure or vacation and instead traveled domestically, staying in domestic hotels.
A network visualization shows the relationship between the words, and the most frequent words are shown with larger nodes and in the center of the figure [38]. The network visualization results, as shown in Figure 3, indicate the frequency of a word based on the size of the label, such as “hotel”, “service”, “room”, “good”, “stay”, “stay”, “view”, “great”, “pool”, and “nice”. Figure 3 shows the top seventy keywords from Table 5, using UCINET 6.0. Since the ten words cited above were most frequently used, these nodes are larger in size than the nodes of the other words.
5.2. Descriptive Analysis Results
The descriptive statistics for all the variables are presented in Table 6. Customer satisfaction ranged from 1 to 5, with an average satisfaction rating of 4.57, which indicates that most of the customers were satisfied with their stay at the hotels in the Marina Bay area.
Regarding sentiment polarity, we used Text2Data for the calculations. As a result, the sentiment polarity values ranged from −0.99 to 1, with an average of 0.36. The higher value, meaning more positive the sentiment (emotion) in the reviews, such as enjoyment, pleasure, delight, and surprise, and the lower the value, the more negative the sentiment (emotion) in the reviews, such as frustration, nervousness, or anger. The value of 0 represents a neutral opinion [67,68].
According to the ratio of unique words to the total number of words in each review, the diversity was calculated. The unique words were counted using readable.com. The results of the diversity score ranged from 0 to 1, and the average was 0.75, which is close to 1. The higher value indicates more unique words in the reviews, as well as fewer redundant words [20,62].
Essentially, readability refers to the difficulty of understanding the online reviews, with a higher value indicating a greater degree of difficulty. In accordance with previous studies, we used the Automated Readability Index (ARI). As a result, the readability values ranged from −10 to 10, with an average result of 0.38 [61].
The length of an online review indicates its number of words [62]. Considering that the length of each review varied greatly, it would be beneficial to deflate and normalize the measurement in the data analytics. In accordance with previous studies, the natural logarithmic transformation of the actual length of the review was used as the measurement [18]. The range of the results in this study was from 0.32 to 3.33, with an average of 1.04, following the logarithm transformation.
5.3. Linear Regression Analysis Results
A linear regression analysis was conducted following the model specified in Equation (1) below. The regression results are presented in Table 5. The variance inflation factors (VIFs) are reported to provide evidence that multicollinearity issues were not a concern in our data, since all the VIFs were well below the typical benchmark value of 10 [18,69]. The Durbin–Watson statistical test score was 1.906, suggesting no presence of autocorrelation. The results in Table 6 indicate that all the hypotheses were significant.
Customer Satisfaction = β0 + β1Polarity + β2Diversity + β3Readability + β4Length + ε(1)
As shown in Table 7, linear regression was used to analyze the relationship between customer satisfaction and the textual reviews. The linear regression analysis included four independent variables: polarity, diversity, readability, and length. The four variables explained 23.9% of the variance (R2 = 0.239). Three variables, “polarity” (ß = 0.91, p < 0.001), “readability” (ß = 0.02, p < 0.1), and “length” (ß = 0.08, p < 0.001), positively affected the customer satisfaction ratings based upon the standardized coefficient values. In summary, “diversity” (ß = −0.08, p < 0.1) had a significantly negative effect on the customer satisfaction.
6. Discussion and Implications
6.1. Discussion
Our results supported H1: overall customer satisfaction is affected by sentiment polarity. Sentiment polarity refers to the sentiment score inside a customer textual review, and it was proven to have the most effect on the customers’ overall satisfaction. It indicates higher sentiment polarity in the customer textual reviews of the luxury hotels in Singapore’s Marina Bay Area, which suggests that more positive words were used in their online reviews than negative words [51]. Positive emotions, including excitement and delight, are expressed by customers with higher sentiment polarity in their online reviews, which is correlated with higher ratings [18,51]. The reviews with high sentiment scores, which positively affected the overall customer satisfaction, featured comments such as: “Friendly staff and the room is impressive. Great view of the Suntec fountain and marina bay area. Room is spacious and full of facilities. The charging port is convenient for international travelers. I loved this place”, with a sentiment score of 0.72; “Wonderful experience great place to stay nice view of bay. Located in central financial region of SG clean and neat near to airport. Easily accessible to all regions”, with a sentiment score of 0.68; and “We had a short staycation and it was a delightful experience! They served decent ala carte buffet breakfast with indoor & outdoor dining area. There are time slots to choose for breakfast and use of swimming pool and gym to avoid overcrowding. Staff are friendly and attentive. Will definitely go back again!”, with a sentiment score of 0.79. We identified many positive adjectives included in the reviews above, such as “wonderful”, “delightful”, and “friendly”.
The second hypothesis in this study was H2: overall customer satisfaction is affected by readability level. However, compared to the other independent variables, readability showed the least effect on overall customer satisfaction. These results suggest that, for primarily satisfied customers in Singapore’s Marina Bay area, a higher readability of online reviews led to a higher level of satisfaction during their stay at in the Marina Bay area. The length and readability of a review text is a two-dimensional quantitative indicator of its understandability, which signifies the extent to which the information content can be absorbed by users, which can reflect their satisfaction [70]. Satisfied customers write clearly in their reviews, as indicated by their choice of sophisticated and professional language. Perceiving a higher degree of professionalism in online reviews influences customers to perceive higher quality in their own stay, thereby increasing their satisfaction [71]. The reviews with high readability scores positively affected overall customer satisfaction, including reviews such as: “Nice and relaxing staycation. Breakfast and dinner were fantastic. Staff make us feel comfortable when taking our order”, with an ARI score of 7.70; “One of the best hotels that I have stayed at from the service to the attention to detail and the view of the bay”, with an ARI score of 8.00; and “A wonderful place to relax with a drink and the view across the water straight to Marina Bay”, with an ARI score of 7.20.
H3: overall customer satisfaction is affected by word length, was also supported by the results. A possible explanation for this is that satisfied customers are motivated to write well-written and in-depth reviews, while unhappy customers tend to use reviews to vent their frustrations and provide less useful information [72]. In this study, it was concluded that the length of a customer’s online review has an impact on the value of their satisfaction. The reviews with high word-length scores positively affected overall customer satisfaction. Most websites monitor review quality by monitoring the review length. Amazon has a minimum word count of 20 words and no maximum. TripAdvisor requests users to describe their travel experiences in at least 200 characters [70]. Among the diverse features of the review text, length and readability are two of the most influential textual features that can affect the quality of reviews, since the review length directly determines the amount of information that a review contains.
The fact that the review length and readability were more influential is evident from their significant and relatively large coefficients in the parameter estimates compared with the other factors. For example: “I had a fantastic time at the hotel. Checking in was swift and professional. The lovely lady who served us was very friendly attentive and helpful when it came to rectifying issues with my reservation and she went that extra mile to make my birthday stay even better. Our first stop was the Lobby Lounge for afternoon tea. Our host who we didn’t get his name seated us and introduced the menu. He went through each item in detail and explained them to us. And also, thanks for the birthday cake surprise! It was very much appreciated. The room was gorgeous and even had treats for us. There’s a cute bear on the bed lots of spaces for our stuff and a rubber ducky in the bathtub. I found the customer service in the executive lounge to be superb. The host introduced us to the executive floor and was very warm and welcoming. However, the breakfast buffet was over-crowded and there wasn’t much variety in their food offerings. Though I will say their waffles and ice cream combination was pretty decent. The friendly staff and great service at this hotel are unmatched. I highly recommend it!”. This review features 234 words.
By contrast, the unsatisfied customers preferred to write short reviews, such as: “Would never stay here again. Bad service. Doesn’t participate in bonvoy and the rooms are super dated. The worst Marriott hotel in Singapore”, with a total word count of 23, and “Terrible. From the moment of check in the experience was horrible. Staff tried to convince us of an upgrade for $30. Only to find out after checking out that my booking for was an upgraded ocean view. Breakfast was to be included but was asked to pay during checkout”, with a total word count of 49.
Lastly, H4: diversity has a positive impact on the overall customer experience in luxury hotels, was found to be incorrect. Diversity in fact had a negative effect on overall customer satisfaction. The number of varied words or unique words used by past customers was proven to not have any effect on overall customer satisfaction. Low diversity also indicated that customers used the same words to convey their disappointment [20]. The reviews with high diversity scores did not necessarily affect overall customer satisfaction, as shown by reviews such as: “Umm... this beautiful hotel has served very horrible food unlikely other 5 star hotels. amazingly bad. I tried 3 different types of breakfast menus. But all are so so or bad. When I asked my friends some nice food in the hotels many of them said the hotel provides good view but not good food. It’s TRUE. I think it’s Far East organization issue. When I went to oasia hotel in Tanjong Pagar i felt same thing. It’s same company... OMG. Next time if you come here don’t include breakfast or try any meals here. But I still love this hotel location and view. That’s why I gave 3 stars otherwise I may give 2 stars.”, with a total unique word count of 81, which, if divided by the total number of words, 112, produces a diversity score of 0.72. In this review, the customer repeated the terms “bad”, “breakfast”, and “same” to convey their negative experience.
6.2. Theoretical Implications
Many hospitality studies using big data focus on finding key attributes that affect customer satisfaction. However, studies applying big data to luxury hotels in Singapore are lacking. In order to gain a better understanding of consumer behavior in, for example, the e-commerce environment, which is dynamic and rapidly evolving, researchers have used online reviews to model and test the influences of reviews, reviewers, products, and consumer characteristics on consumer outcomes [73]. Compared with other studies, this study focuses more on processing the customer textual reviews in luxury hotels in Singapore’s Marina Bay area using different variables and determines the overall customer satisfaction according to these variables. These variables represent the linguistic styles of online reviews, which reflect the writing style of each customer. This study offers three theoretical implications and contributions.
First, this study shows the relationship between two independent variables, linguistic style and overall customer satisfaction. Compared with overall customer satisfaction, customer textual reviews reflect past consumption experiences. This study focuses on using the linguistic styles in which customer textual reviews were written to predict customers’ overall satisfaction in luxury hotels in Singapore’s Marina Bay area. This supports the results of earlier studies by determining how the linguistic style of an online review provides feedback on a hotel’s overall customer experience to future customers and hoteliers.
Second, this study’s findings provide further support for earlier studies regarding the linguistic style of online reviews. Previous studies mentioned that sentiment polarity significantly influences customer satisfaction [18]. This was also shown in the results of this study.
Finally, this study supports earlier studies in the manner in which the patterns of the linguistic styles generated by past customers were examined. This study utilized a sample of 8441 online reviews of seven luxury hotels in Singapore’s Marina Bay area to analyze the business value of customer textual reviews in the hospitality industry. By mining the linguistic styles of customer textual reviews, we were able to handle the vast amount of information.
6.3. Managerial Implications
Through social media and online platforms, consumers express their thoughts and feelings through online reviews of many products and services more freely and openly than before. Businesses may benefit from this abundance of information by adjusting their strategies to gain a competitive edge. Enhancing customer satisfaction is a key to remaining competitive in fiercely competitive markets succeeding in the social media space [47].
Overall customer satisfaction is an important part of the hospitality industry. Previous studies showed that customer satisfaction leads to loyalty [74], and other studies mentioned that satisfaction leads to repurchase intentions [75]. Consequently, overall customer satisfaction has many implications for management. The first finding of this study allows the industry to recognize the attributes affecting overall customer satisfaction through the frequency analysis. Consequently, the luxury hotel management will be able to focus on specific attributes to improve customer satisfaction, allowing management to reach financial goals and other targets.
As a result of this study, luxury hoteliers may be inspired to explore more attributes of customer textual reviews and to investigate the behaviors of online reviews and their relationship to overall customer ratings. Online reviews contribute greatly to the eWOM effect, which has the potential to influence future customer booking decisions in luxury hotels [18]. As a result of this research, industry managers can understand how the styles of textual customer reviews relate to overall customer satisfaction, based on data-mining methodologies. It is important for luxury hotel managers to be aware of and improve the products and services on which their customers provide feedback, but it is also vital to emphasize the linguistic styles of customer reviews. Ultimately, the results of this research will allow the luxury hotel managers in the Marina Bay area in Singapore to determine the key attributes affecting customer satisfaction and interpret customers’ experiences based on their reviews in a post-COVID-19 context.
7. Conclusions and Limitations
7.1. Conclusions
Consumer opinions can either enhance or jeopardize a hotel’s reputation. Negative comments have the potential to tarnish the projected image of a hotel and persuade potential customers to seek competing products/services [44]. Consumers increasingly use the internet to share experiences, especially regarding services provided by all kinds of companies. The opinions of consumers posted online express reliability and can influence the decisions of other consumers when they purchase products or services [44].
With a sample of 8441 online reviews, this study predicted overall customer satisfaction through the attributes of customers’ textual reviews. The words “hotel”, “room”, and “service” were the three most frequently used words. Among all of the independent variables examined, this study showed that sentiment polarity has the greatest effect on overall customer satisfaction. In addition to readability and word length, other variables also contributed to customer satisfaction, albeit in a limited way. The last variable, diversity, showed no significant impact on customer satisfaction.
7.2. Limitation and Future Research Directions
Throughout the research, we found that there were some limitations. As SCTM 3.0 is still in development, only customer ratings, review texts, the duration of data, writer ID, and hotel names could be crawled. Future research may be more heuristic if other variables, such as service quality, can be crawled as control variables.
On the other hand, the data utilized for this study were collected from early 2020, covering a total of 2 years. Future research would benefit from the use of a longer period of time to gain more information. Second, this study is based on customer textual reviews written in or translated into English; however, linguistic styles could differ between languages and culture [18]. Therefore, future research could conduct a similar approach, but compare across languages and cultures. Third, this study did not classify the reviewers according to different levels, which led to varied data. In future research, the reviewer level should be considered to obtain more specific results.
The application of a similar approach to other parts of the hospitality industry, such as airlines, ship lines, restaurants, and MICE (Meetings, Incentives, Conventions, and Exhibitions), would also be of interest. In the hotel industry itself, a specific study on the relationship between the linguistic styles of customer textual reviews and particular products or services would be of additional interest to hotel management.
Supervision, H.-S.K.; Writing—original draft, N.D.H. and A.W. All authors have read and agreed to the published version of the manuscript.
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The authors declare no conflict of interest.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Singaporean luxury hotel performance from 2019 to 2021.
| Q3 2019 | Q3 2020 | Q3 2021 | |
|---|---|---|---|
| Average occupancy rate (%) | 91.1% | 46.7% | 52% |
| Average room rate (SGD) | SGD 471 | SGD 291.7 | SGD 295 |
Operational definition.
| Variable | Operational Definition | Method |
|---|---|---|
| Sentiment polarity | Also called sentiment score, this technique operates by utilizing continuous variables ranging from −1 to +1, corresponding, respectively, to highly positive and highly negative reviews. A higher score indicates a more positive emotion [ |
Text2Data |
| Diversity | This refers to lexical diversity, and its operational definition is the ratio of unique words to the total number of words in an online-review text [ |
Readability score |
| Readability | This refers to the ease with which a text can be understood by a reader. Measured on a numeric scale, it consists of the presentation and the content. An automated readability index (ARI) was developed to operationalize readability [ |
Readability score |
| Word length | This number represents the number of words included in each online review [ |
Natural logarithm of the word count in the review |
| Overall customer satisfaction | Overall customer satisfaction with their experience from one to five [ |
Regression analysis |
Hotels included in the study.
| Hotel Star Rating | Hotel Name | Number of Reviews |
|---|---|---|
| 5-star | Conrad Centennial Singapore | 274 |
| The Fullerton Bay Hotel | 1909 | |
| Marina Bay Sands Hotel Singapore | 2953 | |
| Mandarin Oriental Singapore | 1095 | |
| Pan Pacific Singapore | 608 | |
| Parkroyal Collection Marina Bay | 825 | |
| The Ritz-Carlton, Millenia Singapore | 777 | |
| Total | 8441 |
Summary of overall satisfaction ratings.
| Rating | Frequency | Percentage | Cumulative Percentage |
|---|---|---|---|
| 1 | 265 | 5.5% | 5.5% |
| 2 | 111 | 2.3% | 7.8% |
| 3 | 259 | 5.3% | 13.1% |
| 4 | 808 | 16.7% | 29.8% |
| 5 | 3413 | 70.2% | 100% |
| Total | 4856 | 100% | - |
| Average Score: 4.57 (Std dev: 0.88) | |||
Most frequent keywords for luxury hotels in Singapore’s Marina Bay area.
| Rank | Words | Frequency | Rank | Words | Frequency |
|---|---|---|---|---|---|
| 1 | hotel | 5257 | 36 | thing | 517 |
| 2 | room | 4761 | 37 | sands | 517 |
| 3 | service | 2949 | 38 | guest | 511 |
| 4 | stay | 2533 | 39 | thank | 508 |
| 5 | good | 2471 | 40 | star | 489 |
| 6 | staff | 2405 | 41 | top | 480 |
| 7 | view | 2280 | 42 | wonderful | 473 |
| 8 | great | 2149 | 43 | birthday | 465 |
| 9 | pool | 1606 | 44 | spacious | 462 |
| 10 | nice | 1575 | 45 | city | 439 |
| 11 | Singapore | 1441 | 46 | recommend | 436 |
| 12 | place | 1371 | 47 | visit | 418 |
| 13 | bay | 1347 | 48 | restaurant | 414 |
| 14 | check | 1295 | 49 | comfortable | 414 |
| 15 | food | 1260 | 50 | many | 410 |
| 16 | marina | 1189 | 51 | buffet | 404 |
| 17 | experience | 1136 | 52 | helpful | 390 |
| 18 | breakfast | 983 | 53 | area | 390 |
| 19 | best | 956 | 54 | see | 388 |
| 20 | time | 944 | 55 | family | 386 |
| 21 | friendly | 860 | 56 | awesome | 386 |
| 22 | beautiful | 856 | 57 | Fullerton | 384 |
| 23 | amazing | 834 | 58 | high | 381 |
| 24 | staycation | 816 | 59 | bed | 374 |
| 25 | excellent | 761 | 60 | tea | 367 |
| 26 | night | 722 | 61 | front | 367 |
| 27 | like | 704 | 62 | fantastic | 365 |
| 28 | clean | 694 | 63 | wedding | 364 |
| 29 | love | 688 | 64 | swimming | 353 |
| 30 | day | 637 | 65 | free | 347 |
| 31 | enjoy | 590 | 66 | casino | 347 |
| 32 | come | 586 | 67 | water | 335 |
| 33 | location | 552 | 68 | big | 334 |
| 34 | back | 536 | 69 | facility | 324 |
| 35 | book | 522 | 70 | team | 324 |
Descriptive analysis results.
| Variable | Mean | Std dev | Min | Max |
|---|---|---|---|---|
| Overall customer satisfaction | 4.57 | 0.88 | 1 | 5 |
| Sentiment polarity | 0.36 | 0.46 | −0.99 | 1 |
| Readability | 7.38 | 1.03 | −10 | 10 |
| Length | 1.04 | 0.72 | 0.32 | 3.33 |
| Diversity | 0.75 | 0.24 | 0 | 1 |
Note: Number of observations = 8441; Length is log-transformed value.
Regression results.
| Variable | Coefficient Estimation | Standard Error | t-Value | VIF |
|---|---|---|---|---|
| Constant | 4.21 | 0.03 | 146.32 *** | |
| Sentiment polarity | 0.91 | 0.02 | 48.18 *** | 1.056 |
| Readability | 0.02 | 0.01 | 2.41 * | 1.019 |
| Length | 0.08 | 0.01 | 6.87 *** | 1.075 |
| Diversity | −0.08 | 0.04 | −2.21 * | 1.084 |
Note: Dependent variable = overall customer satisfaction; number of observations = 8441; R2 = 0.239, adjusted R2 = 0.239; F = 640.204; * p < 0.1, *** p < 0.001.
References
1. Singapore Ministry of Foreign Affairs. About Singapore. Available online: https://www.mfa.gov.sg/Overseas-Mission/Washington/About-Singapore#:~:text=Singaporeisacity%2Ca,%2CMalay%2CIndianandEurasian (accessed on 14 April 2022).
2. The World Bank Contributor. The World Bank In Singapore > Overview. Available online: https://www.worldbank.org/en/country/singapore/overview (accessed on 14 April 2022).
3. Invest in ASEAN. WHY Invest in ASEAN?. Available online: http://investasean.asean.org/index.php/page/view/asean-member-states/view/709/newsid/788/singapore.html (accessed on 15 April 2022).
4. Singapore Tourism Board. Singapore’s Tourism Sector Emerges from 2020 with Greater Resilience and Reinvention. Available online: https://www.stb.gov.sg/content/stb/en/media-centre/media-releases/Singapore-Tourism-Sector-Emerges-From-2020-With-Greater-Resilience-and-Reinvention.html (accessed on 11 April 2022).
5. Erh, J. 2021/108 “COVID-19’s Economic Impact on Tourism in Singapore”. Available online: https://www.iseas.edu.sg/articles-commentaries/iseas-perspective/2021-108-covid-19s-economic-impact-on-tourism-in-singapore-by-joey-erh/ (accessed on 13 April 2022).
6. World Tourism Organization. International Tourist Arrival and Tourism Receipts by Country of Destination—Asia and the Pacific; World Tourism Organization: Madrid, Spain, 2020.
7. Singapore Tourism Board. Tourism Receipt by Major Components; Singapore Tourism Board: Singapore, 2021.
8. History, S.G. Singapore Is First East Asian Country to Slip into Recession. Available online: https://eresources.nlb.gov.sg/history/events/3cacf256-82cc-4776-b7f8-83757723b502 (accessed on 13 April 2022).
9. Xie, K.L.; So, K.K.F.; Wang, W. Joint Effects of Management Responses and Online Reviews on Hotel Financial Performance: A Data-Analytics Approach. Int. J. Hosp. Manag.; 2017; 62, pp. 101-110. [DOI: https://dx.doi.org/10.1016/j.ijhm.2016.12.004]
10. Chen, J.I.; Ko, K.C.; Leow, A.; Yap, J.C.; Lim, J. COVID-19 health system response monitor: Singapore. Asia Pacific Observatory on Health Systems and Policies; World Health Organization: Singapore, 2021.
11. Singapore Goverment. Circuit Breaker Extension and Tighter Measures: What You Need to Know. Available online: https://www.gov.sg/article/circuit-breaker-extension-and-tighter-measures-what-you-need-to-know (accessed on 17 April 2022).
12. Singapore Tourism Board. COVID-19 and Its Impact on Tourism; Singapore Tourism Board: Singapore, 2020.
13. SG Clean. Premises Are Awarded with SG Clean Quality Mark after an Official Assessment. Available online: Sgclean.gov.sg/join/for-owners/how-to-be-certified/ (accessed on 17 April 2022).
14. SG Passion Made Possible. Travel Requirements to Singapore. Available online: https://www.visitsingapore.com/travel-guide-tips/travel-requirements/ (accessed on 17 April 2022).
15. Sim, S.; Ng, L.C.; Lindsay, S.W.; Wilson, A.L. A Greener Vision for Vector Control: The Example of the Singapore Dengue Control Programme. PLoS Negl. Trop. Dis.; 2020; 14, pp. 1-20. [DOI: https://dx.doi.org/10.1371/journal.pntd.0008428] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/32853197]
16. McNeill, D. Volumetric Urbanism: The Production and Extraction of Singaporean Territory. Environ. Plan. A; 2019; 51, pp. 849-868. [DOI: https://dx.doi.org/10.1177/0308518X19830699]
17. Padma, P.; Ahn, J. Guest Satisfaction & Dissatisfaction in Luxury Hotels: An Application of Big Data. Int. J. Hosp. Manag.; 2020; 84, 102318. [DOI: https://dx.doi.org/10.1016/j.ijhm.2019.102318]
18. Zhao, Y.; Xu, X.; Wang, M. Predicting Overall Customer Satisfaction: Big Data Evidence from Hotel Online Textual Reviews. Int. J. Hosp. Manag.; 2019; 76, pp. 111-121. [DOI: https://dx.doi.org/10.1016/j.ijhm.2018.03.017]
19. Berezina, K.; Bilgihan, A.; Cobanoglu, C.; Okumus, F. Understanding Satisfied and Dissatisfied Hotel Customers: Text Mining of Online Hotel Reviews. J. Hosp. Mark. Manag.; 2016; 25, pp. 1-24. [DOI: https://dx.doi.org/10.1080/19368623.2015.983631]
20. Xu, X.; Zhao, Y. Examining the Influence of Linguistic Characteristics of Online Managerial Response on Return Customers’ Change in Satisfaction with Hotels. Int. J. Hosp. Manag.; 2022; 102, 103146. [DOI: https://dx.doi.org/10.1016/j.ijhm.2022.103146]
21. Urban Redevelopment Authority. Centrepiece of Singapore’s Urban Transformation. Available online: https://www.ura.gov.sg/Corporate/Get-Involved/Shape-A-Distinctive-City/Explore-Our-City/Marina-Bay.aspx (accessed on 2 May 2022).
22. Urban Redevelopment Authority. History and the Beginnings of Marina Bay. Available online: https://www.ura.gov.sg/Corporate/Get-Involved/Shape-A-Distinctive-City/Explore-Our-City/Marina-Bay/The-Marina-Bay-Story (accessed on 2 May 2022).
23. SG Passion Made Possible. Marina Bay—A Life of Modern Luxury. Available online: https://www.visitsingapore.com/see-do-singapore/places-to-see/marina-bay-area/ (accessed on 5 May 2022).
24. Nasution, D.Z.; Mustika, A.; Arafah, W. Singapore Image as a Muslim-Friendly Destination. Int. J. Innov. Sci. Res. Technol.; 2022; 7, pp. 617-622.
25. Aquino, M. 11 Top Things to Do in Marina Bay, Singapore. Available online: https://www.tripsavvy.com/things-to-do-in-marina-bay-1629856 (accessed on 17 May 2022).
26. SG Passion Made Possible. Marina Bay Sands. Available online: https://www.visitsingapore.com/see-do-singapore/recreation-leisure/resorts/marina-bay-sands/ (accessed on 2 May 2022).
27. Feng, W.; Yang, M.X.; Yu, I.Y.; Tu, R. When Positive Reviews on Social Networking Sites Backfire: The Role of Social Comparison and Malicious Envy. J. Hosp. Mark. Manag.; 2021; 30, pp. 120-138. [DOI: https://dx.doi.org/10.1080/19368623.2020.1775745]
28. Cardenas, D.G.J.; Gurung, D.M.; Han, D.; Ban, H.; Kim, H. The Text Mining from Online Customer Reviews: Implications for Luxury Hotel in Busan. Culin. Sci. Hosp. Res.; 2022; 28, pp. 67-80. [DOI: https://dx.doi.org/10.20878/cshr.2022.28.2.008]
29. Wei, S.; Kim, H.S. Online Customer Reviews and Satisfaction with an Upscale Hotel: A Case Study of Atlantis, The Palm in Dubai. Information; 2022; 13, 150. [DOI: https://dx.doi.org/10.3390/info13030150]
30. Han, H.; Hyun, S.S. Image Congruence and Relationship Quality in Predicting Switching Intention: Conspicuousness of Product Use as a Moderator Variable. J. Hosp. Tour. Res.; 2013; 37, pp. 303-329. [DOI: https://dx.doi.org/10.1177/1096348012436381]
31. Wang, J.; Ban, H.J.; Joung, H.W.; Kim, H.S. Navigations for Hospitality Human Resource Management Research: Observing the Keywords, Factors, Topics under the COVID-19 Pandemic. Information; 2022; 13, 126. [DOI: https://dx.doi.org/10.3390/info13030126]
32. Lam, C.; Raimy, Z.; Ling Teo, Y.; Tan, V.; Kai Ng, Y.; Tan, J. Sustaining a Hotel Business during Crisis: A Singapore Luxury Hotel’s Journey during COVID-19. Muma Case Rev.; 2021; 6, pp. 001-020. [DOI: https://dx.doi.org/10.28945/4862]
33. Li, H.; Ye, Q.; Law, R. Determinants of Customer Satisfaction in the Hotel Industry: An Application of Online Review Analysis. Asia Pacific J. Tour. Res.; 2013; 18, pp. 784-802. [DOI: https://dx.doi.org/10.1080/10941665.2012.708351]
34. Singapore Tourism Board. Tourism Sector Performance; Singapore Tourism Board: Singapore, 2021.
35. Xin, C.K.; Chee, H.Y.; Lee, P. In Focus; Singapore. Available online: https://www.hvs.com/article/9011-in-focus-singapore (accessed on 18 July 2022).
36. Tam, S.S.; Fong, L.H.N.; Law, R. Management response to online review: The case of Hong Kong luxury hotels. Information and Communication Technologies in Tourism 2022 ENTER 2022 eTourism Conference; Springer: Cham, Swizerland, 2022; pp. 123-133. [DOI: https://dx.doi.org/10.1007/978-3-030-94751-4_12]
37. Zhang, X.; Mengying, T.; Kim, H. Exploration of Global Theme Hotels through Semantic Network Analysis of Online Customer Reviews: Focused on Disneyland Hotels. J. Ind. Innov.; 2022; 38, pp. 215-241. [DOI: https://dx.doi.org/10.22793/indinn.2022.38.1.009]
38. Ban, H.J.; Choi, H.; Choi, E.K.; Lee, S.; Kim, H.S. Investigating Key Attributes in Experience and Satisfaction of Hotel Customer Using Online Review Data. Sustainability; 2019; 11, 6570. [DOI: https://dx.doi.org/10.3390/su11236570]
39. Jain, P.K.; Patel, A.; Kumari, S.; Pamula, R. Predicting Airline Customers’ Recommendations Using Qualitative and Quantitative Contents of Online Reviews. Multimed. Tools Appl.; 2022; 81, pp. 6979-6994. [DOI: https://dx.doi.org/10.1007/s11042-022-11972-7]
40. Xu, X. Examining Consumer Emotion and Behavior in Online Reviews of Hotels When Expecting Managerial Response. Int. J. Hosp. Manag.; 2020; 89, 102559. [DOI: https://dx.doi.org/10.1016/j.ijhm.2020.102559]
41. May, Y.; Xiang, Z.; Du, Q.; Fan, W. Effects of User-Provided Photos on Hotel Review Helpfulness: An Analytical Approach with Deep Leaning. Int. J. Hosp. Manag.; 2018; 71, pp. 120-131. [DOI: https://dx.doi.org/10.1016/j.ijhm.2017.12.008]
42. Chan, I.; Lam, L.; Chow, C.; Fong, L.; Law, R. The Effect of Online Reviews on Hotel Booking Intention: The Role of Reader-Reviewer Similarity. Int. J. Hosp. Manag.; 2017; 66, pp. 54-65. [DOI: https://dx.doi.org/10.1016/j.ijhm.2017.06.007]
43. Santos, A.I.G.P.; Perinotto, A.R.C.; Soares, J.R.R.; Mondo, T.S. Feeling at Home While Traveling: An Analysis of the Experiences of Airbnb Users. Tour. Hosp. Manag.; 2022; 28, pp. 167-192. [DOI: https://dx.doi.org/10.20867/thm.28.1.9]
44. Perinotto, A.R.C.; Camarço, J.C.F.; Braga, S.D.S.; Gonçalves, M.F. Perceptions on Services in Ceará-Brazil Luxury Hotels Registered on TripAdvisor. J. Glob. Sch. Mark. Sci.; 2021; pp. 1-20. [DOI: https://dx.doi.org/10.1080/21639159.2020.1808841]
45. Vermeulen, I.E.; Seegers, D. Tried and Tested: The Impact of Online Hotel Reviews on Consumer Consideration. Tour. Manag.; 2009; 30, pp. 123-127. [DOI: https://dx.doi.org/10.1016/j.tourman.2008.04.008]
46. Tang, M.; Kim, H.S. An Exploratory Study of Electronic Word-of-Mouth Focused on Casino Hotels in Las Vegas and Macao. Information; 2022; 13, 135. [DOI: https://dx.doi.org/10.3390/info13030135]
47. Torabi, M.; Bélanger, C.H. Influence of Online Reviews on Student Satisfaction Seen through a Service Quality Model. J. Theor. Appl. Electron. Commer. Res.; 2021; 16, 167. [DOI: https://dx.doi.org/10.3390/jtaer16070167]
48. Gu, B.; Ye, Q. First Step in Social Media: Measuring the Influence of Online Management Responses on Customer Satisfaction. Prod. Oper. Manag.; 2014; 23, pp. 570-582. [DOI: https://dx.doi.org/10.1111/poms.12043]
49. Fu, W.; Wei, S.; Wang, J.; Kim, H.S. Understanding the Customer Experience and Satisfaction of Casino Hotels in Busan through Online User-Generated Content. Sustainability.; 2022; 14, 5846. [DOI: https://dx.doi.org/10.3390/su14105846]
50. Handani, N.D.; Riswanto, A.L.; Kim, H. A Study of Inbound Travelers Experience and Satisfaction at Quarantine Hotels in Indonesia during the COVID-19 Pandemic. Information; 2022; 13, 254. [DOI: https://dx.doi.org/10.3390/info13050254]
51. Geetha, M.; Singha, P.; Sinha, S. Relationship between Customer Sentiment and Online Customer Ratings for Hotels-An Empirical Analysis. Tour. Manag.; 2017; 61, pp. 43-54. [DOI: https://dx.doi.org/10.1016/j.tourman.2016.12.022]
52. He, W.; Tian, X.; Tao, R.; Zhang, W.; Yan, G.; Akula, V. Application of Social Media Analytics: A Case of Analyzing Online Hotel Reviews. Online Information Rev.; 2017; 41, pp. 921-935. [DOI: https://dx.doi.org/10.1108/OIR-07-2016-0201]
53. Qu, Z.; Zhang, H.; Li, H. Determinants of Online Merchant Rating: Content Analysis of Consumer Comments about Yahoo Merchants. Decis. Support Syst.; 2008; 46, pp. 440-449. [DOI: https://dx.doi.org/10.1016/j.dss.2008.08.004]
54. Kim, W.G.; Lim, H.; Brymer, R.A. The Effectiveness of Managing Social Media on Hotel Performance. Int. J. Hosp. Manag.; 2015; 44, pp. 165-171. [DOI: https://dx.doi.org/10.1016/j.ijhm.2014.10.014]
55. Pan, B.; Yang, Y. Forecasting Destination Weekly Hotel Occupancy with Big Data. J. Travel Res.; 2017; 7, pp. 957-970. [DOI: https://dx.doi.org/10.1177/0047287516669050]
56. Westbrook, R.A.; Oliver, R.L. The Dimensionality of Consumption Emotion Patterns and Consumer Satisfaction. J. Consum. Res.; 1991; 18, pp. 84-91. [DOI: https://dx.doi.org/10.1086/209243]
57. Xu, X.; Li, Y. The Antecedents of Customer Satisfaction and Dissatisfaction toward Various Types of Hotels: A Text Mining Approach. Int. J. Hosp. Manag.; 2016; 55, pp. 57-69. [DOI: https://dx.doi.org/10.1016/j.ijhm.2016.03.003]
58. Trivedi, S.K.; Singh, A.; Malhotra, S.K. Prediction of Polarities of Online Hotel Reviews: An Improved Stacked Decision Tree (ISD) Approach. Glob. Knowledge Mem. Commun.; 2022; [DOI: https://dx.doi.org/10.1108/GKMC-12-2021-0197]
59. Fu, W.; Choi, E.K.; Kim, H.S. Text Mining with Network Analysis of Online Reviews and Consumers’ Satisfaction: A Case Study in Busan Wine Bars. Information; 2022; 13, 127. [DOI: https://dx.doi.org/10.3390/info13030127]
60. Kim, Y.J.; Kim, H.S. The Impact of Hotel Customer Experience on Customer Satisfaction through Online Reviews. Sustainability; 2022; 14, 848. [DOI: https://dx.doi.org/10.3390/su14020848]
61. Mariani, M.; Borghi, M. Environmental Discourse in Hotel Online Reviews: A Big Data Analysis. J. Sustainability Tour.; 2020; 29, pp. 829-848. [DOI: https://dx.doi.org/10.1080/09669582.2020.1858303]
62. Zhang, D.; Zhou, L.; Kehoe, J.L.; Kilic, I.Y. What Online Reviewer Behaviors Really Matter? Effects of Verbal and Nonverbal Behaviors on Detection of Fake Online Reviews. J. Manag. Inf. Syst.; 2016; 33, pp. 456-481. [DOI: https://dx.doi.org/10.1080/07421222.2016.1205907]
63. Smith, E.A.; Senter, R. Automated Readability Index; Aerospace Medical Research Laboratories: Springfield, VA, USA, 1967; Volume 66.
64. Tao, S.; Kim, H.S. Cruising in Asia: What Can We Dig from Online Cruiser Reviews to Understand Their Experience and Satisfaction. Asia Pac. J. Tour. Res.; 2019; 24, pp. 514-528. [DOI: https://dx.doi.org/10.1080/10941665.2019.1591473]
65. Olive-Jones, B. What Does the New Google Travel App Mean for Travel Agents?. Available online: https://www.vertical-leap.uk/blog/what-does-the-new-google-travel-app-mean-for-travel-agents/ (accessed on 18 May 2022).
66. SG Clean. Certified Premises. Available online: https://www.sgclean.gov.sg/join/for-owners/certified-premises/ (accessed on 18 May 2022).
67. Vera, V.; Suarez, L.M.M.; Lopera, I.C.P. Sentiment Analysis on Post Conflict in Colombia: A Text Mining Approach Ingeniería de Software View Project Sentiment Analysis on Post Conflict in Colombia: A Text Mining Approach. Asian J. Appl. Sci.; 2018; 6.
68. Amahan, P.A.; Villarica, M.V.; Vinluan, A.A. Technical analysis of Twitter data in preparation of prediction using multilayer perceptron algorithm. Proceedings of the DSIT 2021: 2021 4th International Conference on Data Science and Information Technology; Shanghai, China, 23–25 July 2021; pp. 109-113. [DOI: https://dx.doi.org/10.1145/3478905.3478928]
69. Zhou, K.Z.; Li, C.B. How Knowledge Affects Radical Innovation: Knowledge Base, Market Knowledge Acquisition, and Internal Knowledge Sharing. Strateg. Manag. J.; 2012; 33, pp. 1090-1102. [DOI: https://dx.doi.org/10.1002/smj.1959]
70. Li, L.; Goh, T.T.; Jin, D. How Textual Quality of Online Reviews Affect Classification Performance: A Case of Deep Learning Sentiment Analysis. Neural Comput. Appl.; 2020; 32, pp. 4387-4415. [DOI: https://dx.doi.org/10.1007/s00521-018-3865-7]
71. Sánchez-Franco, M.J.; Navarro-García, A.; Rondán-Cataluña, F.J. A Naive Bayes Strategy for Classifying Customer Satisfaction: A Study Based on Online Reviews of Hospitality Services. J. Bus. Res.; 2019; 101, pp. 499-506. [DOI: https://dx.doi.org/10.1016/j.jbusres.2018.12.051]
72. Wu, P.F. In Search of Negativity Bias: An Empirical Study of Perceived Helpfulness of Online Reviews. Psychol. Mark.; 2013; 30, pp. 971-984. [DOI: https://dx.doi.org/10.1002/mar.20660]
73. Zinko, R.; Patrick, A.; Furner, C.P.; Gaines, S.; Kim, M.D.; Negri, M.; Orellana, E.; Torres, S.; Villarreal, C. Responding to Negative Electronic Word of Mouth to Improve Purchase Intention. J. Theor. Appl. Electron. Commer. Res.; 2021; 16, 109. [DOI: https://dx.doi.org/10.3390/jtaer16060109]
74. Yap, B.W.; Ramayah, T.; Wan Shahidan, W.N. Satisfaction and Trust on Customer Loyalty: A PLS Approach. Bus. Strateg. Ser.; 2012; 13, pp. 154-167. [DOI: https://dx.doi.org/10.1108/17515631211246221]
75. Halstead, D.; Thomas, P. The Effect of Satisfaction and Complaining Behaviour on Customer Repurchase Intention. J. Consum. Satisf. Dissatisf. Complain. Behav.; 1992; 5, pp. 1-11.
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; Williady, Angellie 2
; Kim, Hak-Seon 3
1 Department of Global Business, Kyungsung University, Busan 48434, Korea;
2 Department of Global Business, Kyungsung University, Busan 48434, Korea;
3 School of Hospitality & Tourism Management, Kyungsung University, Busan 48434, Korea; Wellness & Tourism Big Data Research Institute, Kyungsung University, Busan 48434, Korea