Exploring the Importance of Destination Attributes of Sustainable Urban Waterfronts: Text and Data Mining of Tourists’ Online Reviews

Abstract

This study identifies the destination attributes of sustainable urban waterfronts that are frequently mentioned in tourists’ online reviews. We analyzed the influence of these attributes on tourists’ ratings based on stimuli–organism–response theory, and the associations between these destination attributes. The online reviews (both text reviews and star ratings) from TripAdvisor and Google Maps of the sustainable waterfront destinations of the Liuchuan and Luchuan rivers in Taichung city (Taiwan) were collected and analyzed through text and data mining. Destination attributes were grouped into two types: sustainable landscapes (aesthetics, water resource rehabilitation, sustainable lighting, emotional experiences, and low-impact development waterfronts) and sustainable recreational spaces (leisure activities, festivals, inclusive destinations, photography, and tourist experiences). Two destination attributes common to-- both types were identified: nightscapes and waterfronts. These attributes predicted tourists’ ratings through support vector machine analysis. Sensitivity analysis revealed that sustainable landscape-type attributes had a greater impact on tourists’ ratings than the sustainable recreational space type. In addition, three important association rules between twelve attributes were identified and these helped provide information pattern combination attributes from tourists’ comments with support and confidence for the destination attributes. These findings will contribute to urban planning and design in relation to sustainable waterfront destinations. They highlight the need for planners to consider both tourists’ landscapes and recreational needs in order to achieve economic and ecological sustainability.

1. Introduction

Rapid economic development creates many challenges for cities; among these, balancing ecological, economic, and social sustainability is one of the most urgent [1,2,3]. Polluted rivers and damaged natural systems in urban areas have led to the realization that waterfront environments not only create urban ecological diversity [4] but also improve the climate of inland cities that are increasingly affected by climate change [2]. Urban waterfronts are parts of towns and cities that are adjacent to bodies of water, such as rivers or seas [5,6]. They are functional and interactive spaces that link two different systems: land and water [7,8]. Scholars have noted the benefits of successful urban waterfront redevelopment (e.g., forging connections between local residents and visitors and providing spaces for education, healthy living, and wellbeing) [9]. Given the role of sustainable development goals (SDGs) in the development of sustainable cities [10,11], attention is being paid to improving and enhancing the environmental and landscape quality of existing waterfront areas [2,3] and fully using them to provide recreational and tourism functions [12,13] to satisfy the diverse needs of their users. As part of efforts to enhance the quality of urban ecological and social environments, public agencies are looking to make waterfronts attractive tourist destinations in order to stimulate local economic development [13]. Sustainable urban waterfront districts are places where people of all ages and backgrounds can live, work, play, visit, and learn in ways that enhance and celebrate the natural environment and the beauty, diversity, economic vitality, and creativity of a city [8].

Previous studies on urban waterfront destinations have focused on tourism planning [14], tourist experiences [13], and landscaping [7,15]. Few studies have attempted to understand the relationship between tourism and SDGs from the perspective of how sustainable urban waterfronts attract tourists. This question is becoming important given the positive impacts that influencing positive tourist behavior has on economic sustainability (e.g., positive evaluations, word-of-mouth (WOM) intentions, and behavioral intentions) [16,17]. Furthermore, understanding the economic, environmental, and social impacts of tourism requires a detailed examination of a wide range of data [18].

Destination attributes refer to the characteristics and features of a destination that satisfy tourists’ needs [19]. Information about these characteristics and features influence tourists’ behavioral intentions through psychological processes (i.e., perceptions, feelings, and attitudes) [20]. Such information could be obtained through WOM (i.e., face-to-face exchanges of information between tourists’ friends and relatives) [21]. However, with the emergence of the internet, online user-generated content (UGC) and consumer-generated media [21,22], especially comments and ratings, have stimulated information exchange among a much wider range of potential tourists. Well-known travel and search websites such as TripAdvisor, Google Maps, and Lonely Planet provide a large number of online reviews and ratings that could reshape potential tourists’ ideas about a destination, influencing or modifying their destination choices [23]. Scholars have claimed that reviews on these websites represent a more general and widespread form of WOM, namely electronic WOM (eWOM) [24,25], and that the ratings represent customers’ or tourists’ overall satisfaction with the quality, service, or experience of a product or destination [26,27]. For sustainable destination development, eWOM might be more cost-effective [24,28] and influential [29,30] than other sources of information because of the intangible nature of tourism services [31].

Scholars have argued that online reviews could be used as an alternative data source for evaluating sustainable tourism [18]. In the era of big data, tourists leave downloadable qualitative data on the internet through online reviews, photos, and other forms of interaction. However, the influence of online reviews and feedback on destinations offered by public agencies or destination management/marketing organizations (DMOs) has not been explored [32]. Current UGC studies [33,34] have attempted to understand this impact by applying two different approaches (i.e., structured or unstructured), to capture tourist perceptions or awareness [30]. The structured approach relies on quantitative ratings of individual attributes to understand tangible and emotional attributes (e.g., [35]), whereas the unstructured approach relies on open-ended questions that allow respondents to use their own words to freely describe their feelings and perceptions of a destination [36]. The latter approach has been increasingly emphasized by researchers in recent years. However, one of the key challenges is extracting meaningful insights from the large amount of shared text and finding possible patterns and models in this unstructured information [37]. Sparks and Browning [38] have suggested that combining structured quantitative ratings with unstructured textual data could yield a more complete evaluation of users’ online responses and, hence, more useful research results. However, to the best of our knowledge, except for Moro, Rita, and Coelho [39], few researchers have attempted to combine these two types of data to improve our understanding of destination attributes and online ratings in tourist reviews.

This study aims to fill this research gap by exploring and categorizing the most important attributes of sustainable urban waterfront destinations using text mining. Furthermore, data mining techniques are employed to identify the attributes that best predict tourist ratings based on stimuli–organism–response (S–O–R) theory, and the relationships among the attributes. In this theory, the important attributes act as the stimulus (S), tourists as the organism (O), and the ratings for the response (R). These results have useful implications for the planning and marketing of sustainable urban waterfront destinations, sustainable goal management, and marketing strategies of public agencies and DMOs.

2. Literature Review

2.1. Sustainable Urban Waterfront Destinations

The waterfront area refers to the region in a town or city that borders a body of water, emphasizing the relationship between land and water [6]. Urban waterfront areas have historically been important for transportation, trade, and recreation. However, many cities worldwide have focused on revitalizing their waterfronts due to population loss and economic decline in urban areas [11]. The renaissance of urban waterfront areas began in North America in the 1960s and then spread to Europe and Australia [4]. The repositioning of abandoned urban waterfront land as a site for commercial, residential, or recreational development aimed to increase the competitiveness of urban economies and promote local tourism development [40].

In recent years, global trends in urban waterfront development have transformed and rebranded districts [4]. In particular, creating themed landscapes has made tourists key targets of urban waterfront areas, which have been transformed into visual spectacles by uplifting theatrical decorations [41] and accelerating commodification [3]. The sustainable revitalization of urban waterfront areas is a crucial aspect of urban development. Many cities are considering the potential benefits and challenges of sustainably redeveloping their waterfronts [6]. The sustainable development of urban waterfronts aims to regenerate underutilized or degraded waterfront areas, involving ecological, cultural, social, economic, and political sustainability [42]. The redeveloped urban waterfronts should better serve local and regional populations and ecosystems [43]. Redeveloped urban waterfronts have diverse identities and multiple meanings, supporting the use of different types of cultural and recreational activities, promoting the wellbeing and quality of life of each individual in a sustainable environment [40,44].

According to Griffin and Hayllar [13] and Jones [45], there are two types of sustainable urban waterfront development from the perspective of sustainable tourism planning. The first of these involves maintaining the original use but incorporating recreation and tourism development; however, the original use remains the most important consideration. The second involves a complete transformation that sets tourism and recreation as the primary goal of development—aiming to create a marketable cultural attraction that promotes public interest through urban festivals and events [46]. Thus, public agencies, DMOs, and local stakeholders act as catalysts for redevelopment by organizing and promoting special urban festivals with which to stimulate local change and attract tourists [3]. Simultaneously, the use of iconic images and symbols is emphasized in order to sell products and experiences, resulting in the “aestheticization of urban space” [47]. Accordingly, urban waterfront areas have become a “themed background” for entertainment, recreation, and tourism [48]. Thus, rehabilitated urban waterfronts not only revitalize and ecologically enhance overlooked urban areas but also reinvent them as destinations favored by the tourism industry [46].

2.2. S–O–R Theory

The stimulus–organism–response (S–O–R) theory is an extension of the S–R theory, which Mehrabian and Russell [49] applied to environmental psychology to explain and analyze the relationship between the environment and human behavior. This theory suggests that stimuli (S) affect people’s internal affective evaluations (O), which in turn lead to approach or avoidance responses (R). Therefore, the role and relationship of the three components (S, O, and R) are considered in the theory. S are external environmental factors that affect an individual’s internal state and are conceptualized as the effects of stimulating an individual [50]. O are the internal processes and structures that intervene between external stimuli and an individual’s subsequent actions and responses, primarily in terms of emotional and cognitive states [51]. Finally, R, which was originally theorized as a consumer’s “approach or avoidance behaviors” [49], has been conceptualized in empirical studies as a response to an outcome component, conceptualized as a consumer’s final outcome and/or decision, such as the use of behavioral or purchasing intentions as predictors [52,53].

The S–O–R theory has been empirically demonstrated in several studies. Scholars, for example, have used online photos and text in restaurants [52] as S to understand the effect of positive and negative online reviews or to discuss the effect of destination attributes on honeymoon travel satisfaction and experience [53]. This exploratory study applies the core concept of S–O–R theory to discuss the relationship between people and the environment, collect tourists’ destination attributes of urban waterfront areas through online reviews, use the attributes mentioned by tourists in their reviews as environmental stimuli, and use online tourists’ ratings on TripAdvisor and Google Maps as R to understand tourists’ experiences of urban waterfront areas.

2.3. Destination Attributes

According to Lew [54], destination attributes are an amalgamation of different elements that attract tourists to a destination (e.g., beauty, shopping opportunities, cultural exchanges, infrastructure, safety, and activities). Echtner and Ritchie [19] have proposed a triple bipolar structure for the meaning of destination image, comprising “attributes-holistic”, “functional-psychological”, and “common-unique”. This was proposed in order to highlight how destination attributes are related to the perceptions of a destination and tourists’ experiences [55]. Furthermore, according to Prayag’s [56] definition, destination personality is linked to tourists’ cognition of destination features. This could aid tourists in forming and retaining a collection of positive and distinctive associations with their destinations.

In addition, various attempts have been made to categorize specific destination attributes. Gearing, Swart, and Var [57] categorized destination attributes into the following five main groups:

• Natural, social, and historical factors;
• Entertainment and shopping facilities;
• Infrastructure;
• Food;
• Shelter.

In addition, Cooper, Fletcher, Gilbert, and Wanhill [58] categorized destination attributes into a “4A” framework, encompassing attractions, accessibility, amenities, and ancillary services. Buhalis [59] extended this to the “6A” framework by adding available packages and activities. Recently, Moon and Han [60] used 33 items related to local culture, activities and special events, local hospitality, infrastructure, accessibility, destination management, quality of services, and quality of goods purchased. Schlessinger, Cervera-Taulet, and Pérez-Cabaña [61] used nine items to measure destination attributes, including local infrastructure, natural and cultural resources, destination cleanliness, and the hospitality of local people. Regarding sustainable urban waterfronts, scholars suggest that their key attributes include protecting the environment (e.g., natural ecosystems and the recycling of resources) and enhanced present status (e.g., conservation of the genius loci (the historic spirit of a particular space); new public space, views, and perspectives; and a mix of different uses) [7]. Against this background, scholars have called for sustainability studies on urban waterfront development [62,63]. Several researchers have indicated the multidimensional/multi-item nature of destination attributes, which depends on the specific study context [60,61]. In particular, few studies have attempted to measure the attributes of sustainable waterfront destinations in tourist online reviews. Therefore, we propose the following research question:

Is it possible to obtain the attributes of a set of urban waterfront destinations using online reviews?

In the past, the development of urban waterfronts focused more on the environmental functions of water infrastructure and less on the social functions of urban space [40]. In addition, through the transformation of urban development, this space provides people with comfortable and healthy recreational and relaxing environmental functions [43]. Past behavioral studies have mentioned that tourists’ final destination decisions are often based on comparing the attributes of alternative destinations; hence, understanding destination attributes is important for DMOs [55]. A destination’s ability to attract tourists often depends on tourists’ perceptions of the potential benefits provided by the destination’s attributes [64]. As tourists engage in related activities at a destination, these attributes become formative elements of their experiences [55]. It has been suggested that destination attributes (accommodation, attraction, beverages, and transportation) significantly influence tourist satisfaction [65]. In addition, some scholars have found that the landscape, nightscape, and walkway of an urban waterfront influence people’s satisfaction with the space [40]. Therefore, the qualities of destination attributes determine tourist satisfaction as well as future revisit intentions, WOM promotion [28,66,67], service experience, and loyalty (e.g., [61]). In competitive markets, destination attributes play a key role in maintaining competitive advantage [16] and have a meaningful impact on forming a destination’s image [55]. In addition, previous research has shown that the associations between specific destination attributes and tourist responses are contextual and should be measured to reflect the specificity of a destination’s characteristics [68]. For example, Hui, Wan, and Ho [69] found that the link between destination attributes and tourists’ overall (dis)satisfaction differed across geographic regions. Therefore, we adapt the S–O–R outline to analyze tourists’ ratings and proposed the following hypothesis:

Hypothesis: 

The attributes of urban waterfront destinations are predictors of tourist ratings.

2.4. Text and Data Mining in Online Reviews

The United Nations’ SDGs mention tourism sustainability as an issue of global significance [70]. Accordingly, scholars have argued that balancing the economic, environmental, and social impacts of tourism requires detailed and updated data [21]. Among potential data sources, social media data might be effective for assessing sustainable tourism [18]. Previous studies have emphasized the importance of social media in tourism [71]. With the rapid development of the internet, tourists are actively posting UGC on social media and other online platforms [21,22]. UGC and online reviews enable individuals to provide feedback about destinations while they describe experiences shared by individual users through Web 2.0-connected devices [72]. As UGC about a destination accumulates, its visibility increases [73]. Consumers’ online reviews represent a valuable source of information that influence brand awareness, customer relationships [74], and travel-related decisions [23]. Online review content could be used to understand consumer perceptions, preferences, and attitudes [21]. While scholars have understood the importance of monitoring tourist opinions and satisfaction with destinations [75,76], the representativeness and completeness of UGC data present a challenge for research [77]. Nonetheless, UGC analysis remains a powerful tool [21,76] for planning and marketing travel destinations in the internet era.

According to Jia [21], online ratings and reviews are two common forms of UGC related to tourism and travel. Ratings are graded into five levels and thus comprise direct, quantitative indicators of tourists’ evaluations of each aspect of a supplier’s or destination’s performance and their overall satisfaction. Reviews are short texts that qualitatively state the user’s opinion about a supplier or destination; they are extensions and explanations of ratings and describe the causes of (dis)satisfaction. Prior tourism studies have identified the overall attributes of travel destinations through open-ended questionnaires or interviews [55]; however, the availability of secondary data in the form of online reviews and UGC has made qualitative analytic studies easier to conduct, and these have begun to emerge in abundance [78]. For example, studies have conducted content analyses of textual data from online reviews [79,80]. Content analysis refers to the systematic and objective analysis of text features based on patterns and frequencies in textual data [21]. In tourism research, attributes or features are selected using frequently occurring words or term frequency as an inverse to document frequency [81,82]. In a study of destination attributes, Lu and Stepchenkova [83] analyzed 26 attributes from 373 TripAdvisor reviews in seven categories relating to ecological hotel services. Levy, Duan, and Boo [84] analyzed the content of negative hotel complaints from 10 popular online review sites and found that the most common complaints were related to front desk staff, bathroom, room cleanliness, and guestroom noise issues. In the above cases, the data were manually labeled; however, in recent years, data mining techniques have been used [39].

Data mining is a new technology that has developed rapidly in recent years. This is the process of extracting and recognizing useful information from large-scale databases and obtaining knowledge using statistical, mathematical, artificial intelligence, and machine learning (ML) techniques [85]. It could be used in almost any research field that requires data analysis [86]. In the tourism field, data mining techniques could predict the potential value of each customer, reduce the cost of attracting new customers, and strengthen the relationship between hotels and regular customers [39].

According to Turban et al. [85], data mining is divided into three mutually influential steps:

• Pre-modeling (research questions, data evaluation, and preparation for data mining);
• Modeling (selecting analytical techniques, analyzing data, evaluating results, and final model identification);
• Post-modeling (applying and tracking the outcomes of data mining models).

Moro, Cortez, and Rita [86] suggest that, in the overall modeling process (which includes data comprehension, preparation, modeling, and evaluation), preparing data for modeling is one of the most critical steps, especially the selection of variables that best describe the characteristics of the problem (feature selection).

While data mining focuses on structured numerical data (e.g., highly formatted data in a database), text mining focuses on unstructured and semi-structured text data (i.e., texts in social media feedback that are not defined or organized in any form) [86]. Text mining is a common technique used to analyze online reviews [21]. Typically, research aims to identify hidden patterns in online reviews rather than the text reviews themselves [39]. To achieve this, research questions were modeled through the impact of features (independent variables) on user ratings (dependent variables), demonstrating an example of a supervised learning problem in ML [86]. Given these capabilities, this study adopted an ML-based approach to address its research questions.

3. Methods

The two study sites were the Liuchuan Waterfront Trail and the Luchuan Waterfront Corridor in Taichung city, Taiwan (Figure 1). We chose Taichung as the setting of the study because it is the second-largest city in Taiwan, with millions of tourists every year. In recent years, Taichung’s water resource rehabilitation has been internationally recognized (e.g., Japan’s Good Design Award (Best 100) and the FIABCI World Prix D’excellence Awards) [87]. In addition, we chose to study these waterfront destinations because one of them, Liuchuan, is one of the four major rivers in the center of Taichung city [88,89]. As the city developed, the water quality of these two rivers became polluted due to wastewater discharge, and their ecosystems deteriorated, eroding the urban waterfront environment and negatively affecting the city’s image [90]. The Liuchuan river waterfront was improved in 2015 by installing a fully underground water purification plant to treat domestic sewage; the quality of the river’s water environment was improved through sustainable ecological remediation [88,89]. In addition, extending the surrounding green belt connected the riverbank space and facilitated flood control, water quality improvement, and landscape creation to provide tourists with a more natural and ecological experience [90]. The scenic riverbank has started to become an emerging popular waterfront destination in urban areas, with pleasant scenery during the day and bright lights at night, combining aesthetics and recreational use [87,88]. The Luchuan waterfront is the highlight of Taichung’s “Little Kyoto” project dating to the Japanese occupation period in 1900 and was one of eight major nocturnal scenic areas in Taichung at that time [88,89]. The improvement of the water environment prioritized water quality, flood control, and the creation of ecological habitats [90]. To provide tourists access to the river, the surrounding recreational green space was expanded and developed into an inclusive playground, the “Luchuan water purification park”, which is to become a shaded river walkway with a people-friendly, scenic riverbank to attract tourists [89]. Thus, the remediated waterfront landscape was ascribed a new meaning and symbol, relying on the provision of recreational and tourism functions as key elements of its reshaping into a visually appealing and sustainable urban waterfront destination [90]. In addition, these elements have the potential to generate large amounts of data.

The data for this study were obtained from searchable and freely downloadable online sources, namely 2765 Chinese comments posted by domestic and international tourists on TripAdvisor (https://www.tripadvisor.com.tw/ (accessed on 4 November 2023), search keyword = 柳川藍帶水岸, 新盛綠川水岸廊道) and Google Maps (https://www.google.com.tw/maps/ (accessed on 4 November 2023), search keyword = 柳川水岸景觀步道, 綠川水岸景觀步道) relating to the Liuchuan Waterfront Trail and Luchuan Waterfront Corridor in 2018–2023, which were randomly collected over a period of three months; the collected data were used to construct the variables needed to establish the model [91]. Tourist reviews were selected based on their completeness, usefulness in terms of information, and variability across travel seasons. There were many features to choose from in online reviews; TripAdvisor and Google Maps’ online tourist reviews each have data relating to multiple variables. For this study, we selected destination attributes in online reviews (nominal variable: 1 = not mentioned, 2 = mentioned) as the independent variables in the model, and tourists’ ratings (1 = not very satisfied, 5 = very satisfied) were treated as the dependent variables. However, collecting all of the attributes made it difficult to analyze how each feature affected tourist ratings in the subsequent data-mining model using a support vector machine (SVM). Therefore, the number of destination attributes and their categorization were determined in this study based on word frequency and discussions between two experts on destination attributes and sustainable development [91]. SVM is a data-driven approach that attempts to minimize the upper bound of the prediction error and has better prediction results [92], which helps clarify the relationship between destination attributes and tourists’ ratings in this study (Figure 2).

4. Results

4.1. Destination Attributes of Sustainable Urban Waterfronts

In this study, the attributes of negative descriptions were manually checked and excluded before word frequency analysis was performed. Python 3.12.2 was used to tokenize the Chinese comments. In Figure 3, the absolute word frequency analysis (total number of occurrences) employed in the word cloud showed that “nightscape” was the most frequently mentioned destination attribute in visitors’ online reviews, followed by “leisure activities”, “aesthetics”, and “water resource rehabilitation”, presented in relatively large letters, and other less frequently mentioned attributes are presented in relatively smaller letters. To categorize the destination attributes of sustainable urban waterfronts [91], this study integrated keyword analysis to extract the most important terms proposed by Godnov and Redek [93] and Toral et al. [30] and the first step of McCallum and Nigam’s [94] framework. First, two scholars/experts from two universities, with at least five years of academic research and industry experience in tourism and landscape, were asked to manually select words and terms associated with mentions of up to 12 destination attributes from a randomly selected set of 400 reviews (the decision to use these 12 attributes was based on the opinions of the two experts). Second, a further 200 reviews were randomly selected from the dataset (excluding the 400 reviews used previously), and the 12 destination attributes that were included in the reviews were identified. If a comment contained any of the 12 destination attributes, it was considered to have a corresponding destination attribute (comments could contain more than one destination attribute). Based on the consideration of the characteristics of study sites [91], the results show two types of destination attributes: sustainable landscapes (i.e., aesthetics, water resource rehabilitation, sustainable lighting, emotional experience, and low-impact-development (LID) waterfronts) and sustainable recreation spaces (leisure activities, festivals, inclusive destinations, photography, and tourist experience). Two destination attributes (nightscape and waterfront landscape) were common to both types.

4.2. The Impacts of Destination Attributes on Tourists’ Ratings

Based on the results of the previous analyses, we used the remaining 2165 reviews in which the 12 destination attributes were treated as independent variables and tourist ratings were used as dependent variables to develop an SVM analysis model showing how destination attributes influence tourists’ ratings for sustainable urban waterfront destinations. The model’s accuracy was evaluated through a k-fold cross-validation procedure, in which the entire dataset was divided into k-folds [95]. The k-value in this study was set to 10 [96], which means that 90% of the data were used for the training model and the remaining 10% were used for the testing model. The prediction scores were calculated once each time. In this study, “training-testing” was performed 10 times, so ten-fold cross-validation was performed a total of 20 times for the training and testing models. The final scores were calculated from the average of the 20 performances to assess the test model’s accuracy on the novel data. Next, the mean predictive results of the modeling were evaluated by calculating the mean absolute error (MAE) and mean absolute percentage error (MAPE). MAE is the average of the differences between the actual and predicted values, whereas MAPE is the average of each absolute error divided by the sum of the actual values [95]. Thus, in this study, the MAE and MAPE indicators are the means of all absolute errors and absolute percentage errors, respectively.

With respect to the range of one to five used for tourists’ ratings on TripAdvisor and Google Maps, the modeling analysis results show that the MAE of the non-linear model’s SVM was 0.646 (less than one), demonstrating that the predicted value was close to the real score. Using the MAPE, this error was converted into a percentage, and the average error between the predicted and actual scores was 20.38%. Figure 4 shows the relationship between the actual scores (x-axis) and absolute error (y-axis). This shows that the model performed slightly better when predicting high scores, whereas low scores tended to result in higher errors due to poorer representativeness. The model was biased, resulting in under-prediction (over-prediction) for low (high) scores, demonstrating the limitations of the model’s predictive properties. However, although the research model is not completely accurate for every review, effective insights were found in other studies with a MAPE of approximately 27% [39,97], suggesting that the present study’s analysis constitutes a valid approximation of the ratings.

To further understand the importance of destination attributes in tourist ratings, this study conducted data-based sensitivity analysis (DSA). DSA was calculated by randomly selecting several samples from the entire dataset [97]. In this study, 20 DSA calculations were performed, and their average was taken as the correlation of each destination attribute to strengthen confidence in the analysis results. The 12 highest correlations were as follows:

• Aesthetics (0.30%);
• Leisure activities (0.11%);
• Water resource rehabilitation (0.09%);
• Waterfront landscape (0.09%);
• LID waterfront (0.08);
• Photography (0.08%);
• Nightscape (0.07%);
• Inclusive destinations (0.06%);
• Tourist experiences (0.05%);
• Emotional experiences (0.04%);
• Festivals (0.02%);
• Sustainable lighting (0.01%).

In summary, as the destination attributes and tourists’ ratings in the S–O–R framework, the 12 most important destination attributes were closely related to tourists’ ratings. Among these, the relevance of landscape attributes is 52% and recreation attributes is 32%; thus, destination attributes related to sustainable landscapes seem to have a greater impact on tourist ratings than sustainable recreation space attributes (Figure 5).

4.3. Associations between Destination Attributes

Apriori algorithm analysis identified three association rules with lift values greater than one (suggesting the importance of these association rules;

Table 1. Association rules linking sustainable landscape attributes with sustainable recreation space attributes.

Rule	Consequence	Antecedent	Support	Confidence	Lift
1	Leisure activities	Photography	17.577	37.654	1.724
2	Nightscape	Sustainable lighting	12.839	37.746	2.418
3	Aesthetics	Nightscape	5.353	37.838	1.712

) [98]. The first association rule states that 486 of the 2765 reviews mention photography, and of these 1051 reviews, approximately 38% mention leisure. The second rule is that 355 reviews mention sustainable lighting, and approximately 38% of these mention nightscapes. The third rule is that 148 reviews mention nightscapes, and approximately 38% of these mention aesthetics. Overall, the association rules suggest that it is necessary to consider destination attributes related to both the sustainable landscape type (i.e., aesthetics) and the sustainable recreation space type (i.e., leisure activities). Associations with a lift value of less than one had too low a chance of being mentioned and, therefore, were not discussed in this study.

5. Discussion and Implications

This study used data mining techniques on tourists’ online reviews to understand the relationship between destination attributes of sustainable urban waterfronts and tourists’ online ratings. The results show that tourists’ reviews mentioned both tangible and intangible destination attributes, relating to both sustainable landscapes (i.e., aesthetics, water resource rehabilitation, sustainable lighting, emotional experience, and LID waterfronts) and sustainable recreational spaces (leisure activities, festivals, inclusive destinations, photography, and tourist experience). Two destination attributes were common to both categories: nightscape and waterfront landscape. These 12 attributes were the best predictors of how tourists rated sustainable urban waterfront destinations. Furthermore, sensitivity analyses showed that sustainable landscape-type attributes had a greater impact on tourists’ ratings than sustainable recreational space-type attributes. In addition, three important rules of association between the two types of destination attributes were identified. These findings suggest that public agencies should consider tourists’ needs for recreation and landscapes to provide a unique and meaningful tourism experience when planning and designing sustainable urban waterfront destinations. When satisfied, tourists’ needs could become a competitive advantage for sustainable waterfront destinations and help realize sustainable economic development. The following section identifies the key contributions of this study.

Tripadvisor and Google Maps are two of the largest online review platforms used by tourists; their online reviews influence the perceptions and choices of potential tourists. The destination attributes identified in online reviews are often related to tourists’ perceptions and satisfaction [64]. Although tangible attributes could be easily defined by researchers and assessed by tourists, the collection of intangible attributes, such as emotional experiences, is more difficult to identify because they are generally subjective and expressed in various terms during the travel period [16,19]. This study relies on tourists’ ability to express their opinions, views, and feelings about a destination in their own words in free-text online reviews. The results yielded a list of tangible and intangible attributes. The sustainable landscape-type attributes were similar to those identified by [2,4]. They suggested that the protection of the water environment and the transformation of emerging consumer landscapes into landscapes meant for new uses contribute to the establishment of urban waterfront sustainability. The sustainable recreational space-type attributes are similar to those found by Xie and Gu [3], Aiesha and Evans [12], and Griffin and Hayllar [13]. These attributes suggest that waterfront areas are finite, non-renewable, and valuable natural assets that attract and provide recreation for both urban residents and tourists [8,40]. Thus, this study makes a valuable contribution to the SDGs, specifically targeting SDG 15 (Life on Land) and SDG 11 (Sustainable Cities and Communities), with a focus on urban waterfront destinations. Organizations responsible for managing urban waterfront destinations can enhance tourist satisfaction and further encourage repeat visits by implementing of sustainable management strategies. This becomes particularly crucial in addressing the lasting deprivation of urban waterfronts and informing tourists’ decision-making processes. The destination attributes common to both types were nightscapes and waterfront landscapes. This is similar to the study by [40], who recognized that urban waterfront destinations perform well in landscapes, nightscapes, and walkway spaces.

This finding follows Kostopoulou’s [99] observation that sustainable urban waterfronts have the potential to serve as creative environments that attract tourists. As Giovinazzi and Moretti [7], Rahana and Nizar [100] have shown, although the consequences of sustainable urban waterfront development are related to the original characteristics of the host city, other factors (water and environmental quality, mixed use, and recreational development) are necessary considerations when achieving sustainability in urban waterfront development. Against this background, by clearly identifying important destination attributes in tourists’ reviews, the second main contribution of this study is that its findings show how public tourism agencies need to shift their reliance on traditional data collection methods (e.g., questionnaires and surveys). The opinions and perceptions of tourists about sustainable waterfront destinations in online reviews are seldom collected or analyzed by public agencies [13]. Different data collection methods also contribute to S–O–R theory. In this study, destination attributes were collected through online reviews, which are unstructured data used to extract environmental stimuli and evaluate responses to these stimuli. This is in contrast to past studies [53], wherein S–O–R was understood in terms of structural information (e.g., questionnaire surveys). Social media information has become an important reference channel for travel decision-making. In the future, if S–O–R research is combined with text mining to explore environmental and behavioral relationships, more marketing information and market potential could be obtained.

TripAdvisor and Google Maps users provide online reviews in two main ways: by entering a text review in the free-text area and by offering a quantitative rating between one and five. Textual reviews often contain interesting but hidden user sentiments; however, relatively little research has linked ratings to textual reviews. Therefore, this study conducted knowledge extraction by modeling tourists’ ratings on TripAdvisor and Google Maps to examine how each destination attribute in reviews affected rating scores. The results show that the 12 attributes of sustainable urban waterfront destinations significantly predicted tourist ratings. Thus, this study concludes that tourists’ perceptions of destination attributes, which contribute to memorable travel experiences [55], are the best predictors of their ratings of sustainable waterfront destinations in online reviews. Another major contribution of this study is its determination of how perceptions of destination attributes affect tourists’ online ratings of sustainable urban waterfront destinations and to understand what motivates tourists to post ratings for them. These findings are interesting because, unlike previous research that analyzed a small number of destination attributes [55,61], this study proposed a single valid model based on many destination attributes.

In addition, the sensitivity analysis we conducted identified correlations between attributes, which helps explain how each attribute affects ratings on TripAdvisor and Google Maps. The results show that sustainable landscape-type attributes had a greater impact on tourist ratings than sustainable recreational space attributes. Thus, to promote and to realize more effective sustainable economic development and competitive advantage, public agencies could use these findings (i.e., tourist demand for sustainable landscapes and recreational spaces) as a basis upon which to plan and design sustainable urban waterfront destinations.

Finally, to further understand the destination attributes frequently mentioned in tourists’ online comments, the results identified three important rules of association that link sustainable landscape-type attributes to sustainable recreational space attributes. These findings imply that tourists increasingly demand unique and meaningful travel experiences that are generally associated with intangible destination attributes [101]. In addition to the tangible planning of infrastructure, urban waterfront planning also includes an intangible dimension, which could be a place of/about and for cultures, so that dynamic spatial changes can provide people with knowledge regarding changes in traditional culture and diversity [43]. The planning and design of sustainable urban waterfront destinations should focus on a small number of combinations of tangible and intangible destination attributes instead of adopting an all-encompassing strategy [102]. A final major contribution of this study is the identification of a key set of destination attributes that should be the focus of sustainable urban waterfront development.

6. Limitations and Further Research

Despite its contributions, this study has the following limitations. First, tourists’ general perceptions of Taichung’s sustainable urban waterfront destinations might affect their beliefs regarding the relative importance of destination attributes. For example, past research has found that the location of waterfront destinations affects tourists’ ratings [63]. Thus, the wider context of tourists’ comments and ratings might have biased the text and data mining approach and affected the rankings of destination attributes and their correlation with ratings. Given that this study only collected comments related to Liuchuan and Luchuan in Taichung to confirm the model’s generalizability, further research in other locations is required. In addition, the attributes of the 12 destinations were based solely on the opinions of two experts. For example, future research could use (hierarchical) k-means to determine the optimal number of clusters in different settings, resulting in different (non-overlapping) attributes. Second, tourists’ online reviews in this study are limited to TripAdvisor and Google Maps in Taiwan; future research could incorporate reviews from other websites in other countries. The data collected online could be subdivided according to the country of origin. Therefore, future research should compare the attributes identified in Taiwan’s tourist reviews with those of other countries to understand how sustainable urban waterfront destination attributes attract tourists from other countries. Third, as not all reviews have the same impact on potential visitors, concepts such as reviewer trust or helpfulness could be used to categorize reviews [30]. Because both of these concepts could be collected from websites, future research could focus on the highest-scoring reviews (as rated by other members of the community) or reviews posted by trusted reviewers. Finally, considering the ability of the SVM to decompose relationships between many different attributes, there is a possibility that the pattern might contain more attributes from other sources [39]. Future research could be conducted at different locations or with different attributes to understand how to minimize errors in the identified pattern or how to adjust the pattern to improve prediction. Finally, as many scholars have pointed out, there are still some limitations of the traditional text mining techniques used in this study (e.g., [103]), considering the fact that there are more advanced artificial intelligence (AI) models for textual understanding and topic extraction. It is suggested that future work could explore other text embedding approaches to improve the quality of data processing.