This study explores the perception of a “sense of place” among tourists visiting Hegra, an archaeological site in northern Saudi Arabia, through reviews on TripAdvisor. The 267 reviews reviewed on TripAdvisor between 2020 and 2023 were analyzed with the VADER sentiment po-larity analysis tool and object modeling using the NMF machine learning algorithm. The results highlight positive factors linked to the history and uniqueness of the place while showing some critical issues related to isolation, cost, privatization, and competitiveness. The originality of the research lies in the type of case study chosen, an archaeological site of a country that has recently opened its doors to tourism, and in the pragmatic nature of the investigation, oriented towards the search for possible solutions to be adopted in terms of heritage management based on the feedback received for the development of the tourist destination.

Keywords:

Subject: Arts and Humanities - Archaeology

1. Introduction

The importance of understanding and nurturing the sense of place; has never been more critical in the context of sustainable tourism development. This concept, embodying the intricate web of perceptions, emotions, and cultural meanings that individuals and communities associate with specific locales, offers a unique lens through which the potential of tourism can be both envisioned and critically assessed. In the realm of UNESCO World Heritage Sites, such as Hegra in Saudi Arabia, the senso of place; transcends mere physical attributes, encapsulating the rich tapestry of historical narratives, cultural heritage, and natural beauty that these sites offer. However, the burgeoning popularity of these destinations poses substantial challenges to their preservation, necessitating a balanced approach that respects both heritage and the contemporary quest for authentic experiences.

The digital age, characterized by the proliferation of online platforms like TripAdvisor, has significantly transformed the landscape of tourism feedback mechanisms. Today, tourists actively contribute to a dynamic discourse on their experiences, expectations, and critiques through online reviews. This collective feedback serves not just as a repository of personal narratives but as a valuable dataset for sentiment analysis. By employing sophisticated analytical tools, researchers and site managers can mine these narratives to uncover the emotional undertones of visitor experiences, ranging from enthusiasm to disenchantment. Such insights are indispensable for identifying both the strengths and areas of improvement in the tourist experience, enabling targeted interventions that enhance visitor satisfaction while ensuring the conservation of the site’s authenticity and integrity.

This study represents a preliminary initiative to explore experiential constructs and the perception of a sense of place at the site of Hegra, an iconic remnant of Saudi Arabia’s ancient history. At the heart of this study is a pioneering approach that leverages sentiment analysis and Non-negative Matrix Factorization (NMF) to dissect and categorize the rich tapestry of visitor sentiments expressed in online reviews. This methodology not only illuminates the diverse emotional landscapes associated with the Hegra site but also contributes to a deeper understanding of the elements that shape the sense of place; Hegra offers a fascinating window into a flourishing ancient civilization. Drawing on reviews left on TripAdvisor, the study analyzed the feeling associated with this place. It used the VADER sentiment polarity analysis tool and subject modeling via the NMF machine learning algorithm. This method categorized the opinions into three distinct sentiments: positive, negative, and neutral, providing a nuanced understanding of Hegra visitors’ experiences to address the following research questions:

(a): What are the emerging themes of TripAdvisor reviews about experiential and environmental constructs related to the Hegra site?
(b): How can those themes about the “sense of place” in TripAdvisor reviews be use for the tourism development of the travel destination?

By harnessing the power of big data and machine learning, this research offers a nuanced perspective on visitor perceptions, paving the way for informed strategies that optimize the tourism offering and planning. Through a meticulous analysis of online feedback, this study underscores the pivotal role of sentiment analysis in crafting sustainable tourism practices that honor the essence of place, ensuring that destinations like Hegra continue to captivate and inspire for generations to come.

2. Literature Review

According to UNESCO [1], “Cultural heritage is shared wealth with outstanding universal value, the precious wealth left by human ancestors to future generations, and non-renewable precious resources”. Recently, after Covid-19, it happened that heritages have become places of resilience [2] both for residents [3,4] and workers [5] partly attributing to these places the role of the national building [6,7] through heritage enhancement [8,9] sometimes also running the risk of commodification [10]. However, the sense of place has a subjective value [11] that changes between different targets [12], in particular between tourists and residents [13,14,15,16,17,18] in particular when the value associated with it depends on the perception of the place [19,20].

The presence of tourists as advertisers is advantageous for destinations and businesses, as there is no more compelling promotion than that done by tourists themselves [21]. With TripAdvisor and its vast web traffic, the experiences of dissatisfied tourists can reach an unlimited audience, changing how tourists conceive the image of destinations [22]. Online review platforms offer a system for contributing, rating, evaluating, and consuming content, with social distinctions such as several views, impressions, downloads, subscribers, and comments [23]. Such platforms help businesses, destination planners, and national tourism organizations in strategic planning processes to predict tourist preferences and propose a marketing mix that enhances their experience [24]. Above all, thanks to the presence of solid homophily, tourism communities generate richer data and deeper insights, helping tourist destinations to create experiential offerings in terms of personalization. It is made possible by dynamic, extensive data analysis [25,26].

This paper proposes an innovative approach to the study of the sense of place in the archaeological heritage of Hegra, investigating the valence of reviewers’ sentiment and modeling its subjects via non-negative matrix factorization (NMF), applied to a corpus of tourism reviews drawn from TripAdvisor over 13 years, from 2010 to 2023. This methodology is centered on accurate visitor data, allowing us to explore their feelings and offer an in-depth understanding of the sense of place in this historic and cultural site. Furthermore, this approach is part of an emerging trend in tourism studies at UNESCO World Heritage sites, particularly by adopting a data-driven visitor approach to explore the complexity of the emotional relationship between Hegra’s archaeological heritage and its visitors. While new to the field, this innovative approach builds on previous work that has used similar methods on TripAdvisor in various contexts.

The analysis of feedback on social media such as TripAdvisor has been used to convey content and stimulate curiosity towards historical figures, as in the case of Queen Marie Antoinette of France [27], to analyze the perception of tourism [28] domestic and international based on indicators such as cognitive, emotional and relational experiences [29,30,31,32,33] for the development of a destination's marketing [34], also through the definition of the brand personality based on the Heritage travel experience [35,36,37] but also for understand tourists awareness [38,39] and profile [40,41], and promote best practices [42]. Unlike previously investigated, the proposed case analyzes the archaeological site of Hegra in Saudi Arabia. This country has recently started to promote international cultural tourism, so the site represents something new compared to previous studies.

Hegra, known by the Arabic term Al-Hijr or Al-Ula from the toponym of the nearby modern city, is located in the Hijaz region in the northern part of the current Kingdom of Saudi Arabia. The area inhabited since the Iron Age [43,44] experienced its period of maximum development during the Nabataean kingdom, of which precious funerary evidence remains represented by the monumental tombs dug into the rock [45]. Subsequently, it was first conquered by the Romans, as demonstrated by the inscriptions in Greek and Latin [46]. Then, it became a flourishing settlement in the proto-Islamic era [47]. Recognized as a UNESCO site in 2008 [48], it saw the start of the Franco-Saudi excavation campaign in the same year [49], which will continue with the participation of international teams [50]. has been included since 2016 in the large “Saudi Vision 2030” project [51], and with the opening to tourism in 2019, it is confronted with the challenges of management [52] and sustainability [53]. While new to the field, this innovative approach builds on previous work that has used similar methods on TripAdvisor in various contexts.

3. Materials and Methods

3.1. Study Site and Data Preparation

The research is based on analyzing English-language reviews published on TripAdvisor from 10 November 2010 to 30th December 2023, which were carefully collected using Python and the Scrapy module, exploiting HTML and XML codes for efficient extraction. The 245 reviews collected and saved in a CSV file represent valuable information, reflecting visitors’ perceptions and experiences of this archaeological treasure trove, living testimony to a flourishing ancient civilization. Then, after the careful collection of reviews, a data pre-processing process was implemented using advanced natural language processing (NLP) techniques, comprising six main steps, as follows:

Normalization - This enables all words to be treated uniformly since in NLP, upper and lower case are generally treated differently;
Removal of emojis and non-alphanumeric characters - This includes the removal of punctuation, as these elements can interfere with textual analysis by introducing noise;
Stemming - this is a linguistic process that reduces words to their primary form;
Tokenization - this involves dividing the text into words or "tokens";
Removal of stop words - stop words are prevalent words in a language, such as "the," "the," "and," which are generally removed because they are uninformative.

The next step was transforming reviews into usable data, which paved the way for a decisive step in data mining: “text vectorization.” It has been based on two essential techniques: Term Frequency (TF) and Term Frequency-Inverse Document Frequency (TF-IDF). TF measures how often a term appears in a document, while IDF assesses the importance of a term across the corpus. The combination of TF and IDF results in the TF-IDF score, which quantifies the relative importance of a word in a document compared to the corpus as a whole. The TF-IDF score for word i in document j is defined by the equation [54]:

j=tfi,j.idf(i)

3.2. Exploratory Data Analysis

Six data mining approaches have been used to explore the obtained corpus to understand in detail the experiential and contextual patterns associated with visiting the Hegra site.

Count of reviews over time. It makes it easier to track the evolution of visitor comments over time, enabling us to identify the periods most conducive to comment generation. It was also helpful in understanding which specific events could be associated with the trends observed.

Count of reviews by Rating. Analysis of TripAdvisor ratings, particularly for sites like Hegra, enables an understanding of how visitors rate their experience. These ratings, ranging from 1 to 5 stars, reflect users’ opinions and contribute to knowing the number of reviews per rating to determine trends in satisfaction or dissatisfaction with the site visit.

Distribution of review text length by rating. Analyzing the length of reviews by rating can help to understand whether satisfied or dissatisfied tourists are more inclined to provide substantial detail about their experience. Negative reviews, especially if they are detailed, can provide valuable information for improvement. Conversely, lengthy positive reviews can highlight the strengths of a tourist site.

Tourist nationality. This data highlights which countries Hegra’s visitors come from, as shown by the presentation of the top 30 nationalities.

Occurrence analysis. This exploratory analysis focuses on revealing the most frequent terms using N-grams, as explained by Tripathy et al. [55], which are contiguous sequences of n elements in a text or discourse.

N-gramwi=(wi,wi+1,...,wi+N-1)

N-gram: This indicates an N-gram, where N represents the size of the sequence;
wi : This is the i-th word in the text;
wi+1,wi+2,...,wi+N−1 : The following words in the text form a sequence of N consecutive words.

The size of the N-gram can adopt different naming conventions. Bigram analysis for n=2 and trigram analysis for n=3 were used in this exploration.

Co-occurrence analysis. Exploratory corpus analysis involves determining co-occurrences. This technique illustrates how terms are associated [56]. For this purpose, Ward’s hierarchical clustering technique has been used, progressively merging clusters by minimizing the increase in squared errors at each step [57]. Ward’s method combines clusters by reducing the increase in the sum of squared errors (SSE), aiming for maximum cluster homogeneity [58]:

SSE= ∣Ci∣⋅∣Cj∣∣Ci∣+ ∣Cj∣ ⋅d Ci,Cj2

Where:

Δ(SSE) is the increase in the sum of squared errors due to the merging of two clusters;
∣C_i∣ and ∣C_j∣ are the sizes (number of observations) of clusters i and j, respectively;
d(C_i,C_j) is the distance between the centers of gravity (or centroids) of clusters i and j;
d(C_i,C_j)² is the square of this distance

Then, the Euclidean distance was used to assess similarity within our categorical data set. Accordingly, the distance between each pair of continuous points is measured using Euclidean distance [59]. In an n-dimensional space, the Euclidean distance between two points p and q is calculated using the following formula:

DEuclideanp,q= i=1npi-qi2

Where pi and qi are the coordinates of the points in each dimension.

Occurrence and co-occurrence analyses have been combined in a single figure.

3.3. Proposed Method

The adopted methodology uses sentiment analysis to divide visitors’ opinions into three categories (positive, negative, and neutral) through the Non-negative Matrix Factorization method to extract specific themes from each category. This multi-dimensional approach provides a holistic understanding of visitors’ feelings about the archaeological site and their feelings about it.

VADER-based sentiment analysis is the computational study of people’s opinions towards entities such as products, services, problems, events, topics, and their attributes [60,61,62]. It is based on analyzing user-generated content, providing an in-depth understanding of the opinions expressed [63]. The heart of sentiment analysis lies in solving a polarity classification challenge, an operation aimed at positioning a text on a scale ranging from positive to negative [64].

With this in mind, this study is based on the use of VADER. This ternary sentiment classification tool discerns the nuances between "positive," "negative," and "neutral" sentiments, thus establishing a clear distinction between objective and subjective expressions in our textual data.

Furthermore, VADER sentiment is a rule- and lexicon-based framework for sentiment analysis, supporting intensity estimation [65]. VADER uses qualitative rules and quantitative calculations to generate sentiment scores (positive, negative, neutral) for each text [66]. Evaluation of the distribution of compound scores guided the development of our tool. The compound score, a metric normalized between -1 and +1, is essential for classifying sentences as positive, neutral, or negative [67,68]. Typical thresholds are as follows:

Positive sentiment: composite score >= 0.05
Neutral sentiment: composite score > -0.05 and < 0.05
Negative sentiment: composite score <= -0.05

The VADER composite score results from a specific formula considering each word’s valence scores. This formula normalizes the sum of the valence scores, guaranteeing a constant scale regardless of text length. The normalized formula follows:

Compound Score=Valence 2∑Valence+

∑ Valence is the sum of the valence scores, and α is a normalization parameter (usually a tiny constant).

The reviews are classified by sentiment (Figure 1). The harmful data, numbering 21, reveal a largely unfavorable sentiment, with an average score of -0.291. This trend is accentuated by a concentration of points in the lower quartile of the graph, indicating that the majority of these reviews are strongly negative. By contrast, neutral reviews are few, with just 11 reviews, and have an average score close to zero (0.012). The close grouping of points on the graph highlights a consensus of indifference between these reviews, reflecting a uniformity in the neutrality of sentiments expressed.

However, panorama changes and positive reviews dominate the data set, with 213 reviews. These opinions have a high average score of 0.770, reflecting a definite enthusiasm. The dispersion of points in this category is significant, showing a diversity in the intensity of positive reviews, ranging from moderately to highly favorable. Overall, the reviews were very positive, despite adverse reactions and a few neutral reviews. (Table 1)

3.4. Topic Modeling Using Non-Negative Matrix Factorization

In the study of sentiment analysis, Non-negative Matrix Factorization (NMF) plays a crucial role. This method decomposes a matrix of textual data into two matrices with non-negative elements, thus revealing latent themes in a corpus evaluated as positive, negative, or neutral with VADER.

NMF is recognized for its ability to automatically discover topics in large volumes of text [69]. It has been successfully applied in unstructured text [70,71]. A notable advantage of NMF is the reduction in the number of parameters required in modeling despite its optimization objective being a non-convex problem [72]. Finally, NMF stands out for its ability to perform dimension reduction and clustering simultaneously, facilitating the creation of topic-specific models for unstructured documents [73].

It is an unsupervised learning method for finding meaningful and physically interpretable latent variable decompositions [74]. In the context of sentiment analysis, the Non-negative Matrix Factorization (NMF) method is central to identifying latent themes in our text corpus, using the formula:

VWH

Here, V is the original textual data matrix we wish to decompose, while W and H are the resulting matrices containing the corresponding non-negative basis vectors and coefficients, respectively. This decomposition minimizes the reconstruction error between V and WH [75,76]. For a better understanding, Kuang et al. [77] were able to conceptualize the non-negative matrix factorization (NMF) decomposition of a matrix composed of m words in n documents into two non-negative matrices of the original n words by k subjects and those same k subjects by the original m documents. (Figure 2)

4. Results

4.1. Illustration of Exploratory Data Analysis

A quantitative analysis of the feedback highlights that reviews will fluctuate until 2022 (the first year of effective tourism recovery after COVID-19). In particular, we note a peak in reviews in 2016, the year of the launch of the Saudi Vision 2030 Program, which remained constant for the following year, demonstrating continued use of the site in the next years. 2019 is the first year of opening to tourism on an international level, which marks a change of direction concerning the site's attendance by Saudis and residents. However, an initial positive response in terms of feedback (by number) in 2020 was followed by a severe slowdown due to COVID-19, which made the site accessible only to local tourists. 2022 marks the moment of recovery and highlights a constant trend in the capacity of tourists to engage with the archaeological site of Hegra. (Figure 3)

A qualitative analysis of the visitors’ feedback on the Hegra archaeological site, rated on TripAdvisor, on one hand, reveals a predominantly positive assessment. Indeed, a tiny proportion of reviews, just 8, classify the experience as "Very Bad." This downward trend continues, with four reviews grading the experience as "Bad," underlining the low incidence of dissatisfaction among visitors.

On the other hand, a slightly higher number of people, 11, rated their experience as "Average", indicating neutral satisfaction, which could reflect an expectation of additional qualities or services. However, perceptions improve considerably, with 47 visitors describing their visit as "Very Good", testifying to a positive and enriching experience. This appreciation is even more evident in the "Excellent" category, which accounts for most reviews, with 175 highly positive returns. These glowing reviews underline the excellence of the Hegra archaeological site, suggesting that the vast majority of visitors leave with memorable impressions and a high regard for the place. (Figure 4)

The analysis of tourist reviews on the archaeological site of Hegra reveals intriguing patterns related to the length of comments based on the ratings given. (Figure 5)

Analyzing the feedback in detail, it emerges that reviews categorized as "Very Poor" are relatively brief, averaging 267 characters, suggesting that dissatisfied visitors tend to express their discontent succinctly. On the other hand, "Poor" reviews are significantly longer, averaging 702 characters, which might indicate a tendency to provide detailed explanations about the negative aspects of their experience.

Furthermore, "Average" ratings present a moderate average length of 287 characters, striking a balance between expressing satisfaction and reservation. Surprisingly, in the "Very Good" category, wide variability in review length has been observed, with some comments being extremely detailed, as evidenced by the maximum of 2742 characters. This reveals that some visitors are particularly eager to share their positive experiences.

Finally, although "Excellent" reviews are the most numerous, they display an intermediate average length of 345 characters. However, this category also includes pervasive narratives, up to 2988 characters, underscoring that the most satisfied visitors sometimes take the time to narrate their memorable visit in detail. The outliers observed for the higher ratings confirm this trend towards exhaustive accounts when the experience is wildly appreciated.

This analysis demonstrates that, while the general trend indicates that negative reviews are longer, notable exceptions in the positive categories reveal a desire to share enriching experiences equally detailedly. (Table 2)

Looking at the data processing, a 14.69% rate of missing values for visitor nationality emerges. Despite this limitation, the overall understanding of the audience remains intact, highlighting the predominance of local visitors from Saudi Arabia (33.49%). Although the country promotes international cultural tourism, data demonstrate that the most relevant part of the visitors (quantitatively) is represented by local (domestic) as Saudi citizens or foreign permanent resident citizens (Saudi Ministry of Tourism, 2023). (Figure 6)

From the analysis of the 30 most frequent bigrams and their dendrogrammatic co-occurrence matrix, it is clear that the most recurring words are associated with the place, its definition, and characterization as "world heritage", "Saudi Arabia", and "archaeological site". and its importance as a UNESCO cultural heritage site. (Figure 7)

However, in the subsequent and more detailed analysis of the 30 most frequent trigrams and their dendrogrammatic matrix of co-occurrence, words emerge such as "Nabateans", "pre-Islamic", and "lignite" more linked to the history of the place. (Figure 8)

4.2. Mapping of Topic Modeling Using NMF

For the subject modeling, the number of subjects for the NMF arbitrarily has been determined. Positive reviews are represented in a model. (Figure 9)

Six main themes emerge from the analysis of the keywords. The first is linked to the site’s uniqueness, often compared to Petra for historical reasons and, above all, for an extreme similarity of the funerary complexes. A second theme deals with the importance of the travel experience in terms of organization, particularly appreciated in a completely isolated site. Subsequently, a theme related to the place's perceived beauty and historical importance emerges, promoting a sense of connection with the past. The penultimate theme concerns accessibility from a logistical point of view, i.e., easy access through an efficient road network and historical, following ancient restored caravan routes, which give prestige to the place and value to the experience. Finally, the last particularly significant theme concerns the wide range of attractions, such as the Elephant Rock and the tombs, considered holistic complexes that characterize and emphasize the visit to an archaeological site of cultural interest. (Table 3)

Negative reviews were also processed into a model. (Figure 10)

Four main themes emerge from the analysis of the keywords. The first is linked to the site's isolation; it isn’t easy to access from other tourist sites due to its distance. A second theme concerns the ticket price, which is considered high compared to the service. The penultimate element involves privatizing cultural heritage as an additional element that does not guarantee equity access to public cultural heritage. Finally, the last particularly significant theme concerns competitiveness, mainly when a close correlation with Petra is evident, of which the ability to manage cultural heritage and tourist flows is highlighted (Table 4).

Neutral reviews were also processed into a model (Figure 11).

Two main themes emerge from the analysis of the keywords. Neutral reviews, as the term suggests, are less penetrating. They mainly have a descriptive character and are centered on two major themes: the site, perceived as of great importance (also due to its registration as a UNESCO heritage site), and the tombs, due to their monumentality and beauty. In these reviews, however, a critical approach does not emerge, that is, a personal opinion of the user on the site visited. (Table 5).

5. Discussion

TripAdvisor is an online platform that tends to be hyper-centric, that is, hyper-positive or hyper-negative; however, it is possible to make some valuable considerations for the tourism development of the archaeological site. One of the most significant aspects that emerge from the overall analysis of the reviews is the exceptional nature of Hagra, perceived as a “magical” place due to its history and uniqueness. These two strengths are the ones in which to invest for the promotion of the site because, on the one hand, its historical value represents the economic lever for its marketing promotion as a brand; on the other, its uniqueness makes it sustainable as a niche destination and not for mass consumption, contained tourist flows without losing the sense of exclusivity. However, there are some more challenging aspects that, if appropriately managed, can improve the tourist experience and, consequently, the promotion of the destination.

One of the main challenges the site faces is related to its isolation. For example, while Petra (Jordan) is approximately two hours away from major tourist centers, the distance increases significantly in the case of Hegra. It implies the need to develop a hotel project based on the target of travelers interested in the site, i.e., those with a high cultural level, sensitive to elegance but not pomp.

A second challenge to consider is related to the cost of the visit, which in many cases is perceived as high. However, similar cases exist: for example, the price of Gorilla Tracking (Rwanda), an exploratory tour to see gorillas in their natural habitat, costs $1,500 per person with one year of pre-booking [78] precisely to guarantee a more sustainable management of the park and better control of tourist flows. At the same time, this also implies adopting policies tailored to the target type. For example, in Machu Pichu (Peru), the visit cost includes a reduction for undergraduate students and minors between 3 and 17 years old. In comparison, it offers free entry to children from 0 to 3 years old [79]. Similarly, at the Kakum National Park (Ghana), there are reductions for Ghanaian citizens compared to non-Ghanaians, recognizing the cultural heritage belonging to the local community [80]. But in addition to defining the terms of access, even the visit methods should also be considered through the adoption of codes of conduct, as in the case of the Arizona State Park (USA), which has adopted an “Archaeological Site Etiquette” on how to behave in the presence of artifacts, rock piles, rock art, etc. [81].

A third element of analysis concerns the privatization process. Unfortunately, the public administration is not always able to manage all the cultural heritage of a country, so the PPC (Public-Private Cooperation) represents a facilitator in the management process, as in the case of the Colosseum (Italy), whose ticket office is entrusted to a private company [82]. However, a good outcome of this allocation process also depends on the policy adopted by the government through discussion with the social partners and on the type of agreement between the parties to avoid monopoly and exclusivity of use by the private sector and having the public good as a common objective.

Finally, a fourth element concerns the competitiveness, sometimes unfavorable, of Hegra compared to Petra. However, promoting a tourist destination, such as an archaeological site, aims not to impose itself on another but to emerge among all through a network that enhances the site’s uniqueness. For example, the archaeological site of Pompeii (Italy), which linked its fame to the violent volcanic eruption that destroyed the Roman city, has also included other Roman sites in the same area in its circuit [83], reducing the pressure of tourists within the site and also promoting other places of cultural interest, which are linked to its brand.

6. Conclusions

This research investigates the “sense of the place” in the travel experience through TripAdvisor’s review. Despite the limitation due to the hyper-centrism of the platform, the analysis is useful to obtain valuable data on the strengths of the cultural heritage and, at the same time, highlights the weaknesses for which it is possible to adopt measures aimed at containing or solving the single problems emerged, developing tourism to promote the heritage site as a travel destination.

Funding

This research received no external funding.

Conflicts of Interest

The authors declare no conflicts of interest.

References

UNESCO. Convention concerning the Protection of the World Cultural and Natural Heritage, 1972, Paris.
Dewi, C.; Rofe, M.; Nichols, J. Heritage and the COVID-19 pandemic: the meaning of visitation. Built Heritage 2023, 7(11). [CrossRef]
Li, Y.; Stoffelen, A.; Meijles, E.; Vanclay, F. Local people’s sense of place in heavily touristified protected areas: Contested place meanings around the Wulingyuan World Heritage Site, China. Landscape and Urban Planning 2023, 237. [CrossRef]
Pepe, L.; Schmitz, S. The Sense of Place of Local Mining Heritage in Wallonia. Heritage & Society 2023. [CrossRef]
Marghany, M.; Morgan, N.; Finniear, J.; White, P. Heritage hotels: An exploration of staff experiences in these unique hospitality environments. Tourism and Hospitality Research 2023. [CrossRef]
Wei, H.; Yu, Y; Yuan, Z. Heritage Tourism and Nation-Building: Politics of the Production of Chinese National Identity at the Mausoleum of Yellow Emperor. Sustainability 2022, 14. [CrossRef]
Moscatelli, M. Cultural identity of places through a sustainable design approach of cultural buildings. The case of Riyadh. In International Conference on Sustainability: Developments and Innovations, ICSDI, Riyadh, 19-22 February 2022. [CrossRef]
Wei, H.; Yu, Y.; Yuan, Z. Heritage Tourism and Nation-Building: Politics of the Production of Chinese National Identity at the Mausoleum of Yellow Emperor. Sustainability 2022, 14. [CrossRef]
Moscatelli, M. Cultural identity of places through a sustainable design approach of cultural buildings. The case of Riyadh. In International Conference on Sustainability: Developments and Innovations, ICSDI, Riyadh, 19-22 February 2022. [CrossRef]
Xiang, T.H.; Mohamed, D. The effect of placemaking attributes on tourism experience: a case of study of petaling street in Malaysia. Planning Malaysia 2023, 21(4), 322–333.
Korani, Z.; Sam, F.A comparative study of heritage visitors’ perceptions of time in the modern era, Current Issues in Tourism 2023, 26(5), 807–822. [CrossRef]
Bresler, M. Specifics of perception of historical and cultural heritage by generation Z in the conditions of network existence, In International Scientific Conference Ecological and Biological Well-Being of Flora and Fauna 2023, 420(45). [CrossRef]
Zhou, K.; Wu, W.; Li, T. Exploring visitors’ visual perception along the spatial sequence in temple heritage spaces by quantitative GIS methods: a case study of the Daming Temple, Yangzhou City, China. Built Heritage 2023, 7(24). [CrossRef]
Dabphet, S. Examining the role of heritage-destination attributes on travel constraints and revisit intention using multilevel analysis. Journal of Hospitality and Tourism Insights 2023. [CrossRef]
Coscia, C.; Pasquino, F. Demand Analysis Models to Support Cultural Tourism Strategy: Application of Conjoint Analysis in North Sardinia (Italy). Land 2023, 12. [CrossRef]
Mohanty, R.N.; Mohanta, A. Assessment of stakeholders' perspectives on infill buildings in the historical settings of an eastern India state – Odisha, Journal of Cultural Heritage Management and Sustainable Development 2023. [CrossRef]
Chen, G.; Shi, J.; Xia, Y.; Furuya, K. The Sustainable Development of Urban Cultural Heritage Gardens Based on Tourists’ Perception: A Case Study of Tokyo’s Cultural Heritage Gardens. Sustainability 2020, 12. [CrossRef]
Katahenggam, N. Tourist perceptions and preferences of authenticity in heritage tourism: visual comparative study of George Town and Singapore. Journal of Tourism and Cultural Change 2020, 18(4), 371–385. [CrossRef]
Yang, H.; Qiu, L.; Fu, X. Toward Cultural Heritage Sustainability through Participatory Planning Based on Investigation of the Value Perceptions and Preservation Attitudes: Qing Mu Chuan, China. Sustainability 2021, 13. [CrossRef]
Nayan, N.M.; Jones, D.S.; Ahmad, S.; Khamis, M.K. Exploring the built-environment: heritage trails, values and perceptions. In IOP Conference Series: Earth and Environmental Science, Vol. 881, 5th International Conference on Rebuilding Place 15-16 September 2021, Banda Aceh, Indonesia. Earth Environ. Sci, . [CrossRef]
Rodríguez-Fernández, M.M.; Sánchez-Amboage, E.; Juanatey-Boga, O. Virtual tourist communities and online travel communities. Communication: Innovation & Quality 2019, 435–446. [CrossRef]
Carrasco-Santos, M.J.; Ciruela-Lorenzo, A.M.; Pavón, J.G.M.; Rodríguez, C.C. An online reputation analysis of the tourism industry in Marbella: A preliminary study on open innovation. Journal of Open Innovation: Technology, Market, and Complexity 2021, 7(2), 111. [CrossRef]
O’Connor, P. Managing a hotel’s image on TripAdvisor. Journal of Hospitality Marketing & Management 2010, 19(7), 754–772.
Marine-Roig, E.; Clavé, S.A. Affective component of the destination image: A computerised analysis. In S. Pile (Ed.), Destination Marketing, Routledge 2015, pp. 49–58, ISBN: 9781315720050.
Buhalis, D.; Sinarta, Y. Real-time co-creation and nowness service: Lessons from tourism and hospitality. Journal of Travel & Tourism Marketing 2019, 36(5), 563–582. [CrossRef]
Lam, J.M.; Ismail, H.; Lee, S. From desktop to destination: User-generated content platforms, co-created online experiences, destination image and satisfaction. Journal of Destination Marketing & Management 2020, 18. [CrossRef]
Paige-Lovingood, M. In the Shadow of the Queen: On UNESCO’s Universal History, the Women of the Petit Trianon, and Tourist Meaning-Making. International Public History 2021, 4(2), 127–137.
Pereira, T.; Lima, F.B.C.; Leoti, A. Tourism management and planning at a UNESCO heritage site: a study of the Ruins of the Jesuit Mission of São Miguel das Missões, Brazil [Gestão e planejamento turístico de um patrimônio mundial da UNESCO: um estudo sobre as Ruínas da Missão Jesuítica de São Miguel das Missões, Brasil]. PASOS Revista de Turismo y Patrimonio Cultural 2022, 20(2), 285–298.
Nguyen, T.H.; Ağbay; N.C.; Çakar, K. Visitors’ experiences of UNESCO World Heritage Site: evidence from Göbeklitepe, Türkiye. Journal of Hospitality and Tourism Insights, 2023. [CrossRef]
Sánchez, F.T. Comparison of Memorable Tourist Experiences Based on Collections of Reviews from Trip Advisor: Acropolis of Athens and Royal Alcazar of Seville, in V. Katsoni, & A.C. Şerban (Eds.), Transcending Borders in Tourism Through Innovation and Cultural Heritage, 8th International Conference of the International Association of Cultural and Digital Tourism, Hydra, 1-3 September 2022, pp. 943-960. [CrossRef]
Baniya, R.; Dogru-Dastan, H.; Thapa, B. Visitors' experience at Angkor Wat, Cambodia: evidence from sentiment and topic analysis. Journal of Heritage Tourism 2021, 16(6), 632–1645. [CrossRef]
Tan, C.L.; Yeo, S.F. Tourist experience on traditional pastries in UNESCO heritage city. British Food Journal 2020, 122(12), 3885–3897. [CrossRef]
Stoleriu, O.M.; Brochado, A.; Rusu, A.; Lupu, C. Analyses of Visitors’ Experiences in a Natural World Heritage Site Based on TripAdvisor Reviews, Visitor Studies 2019, 22(2), 192–212. [CrossRef]
Magalhães, M., Braga, J. L., Borges, I. Improve the Traveler’s Experience: Model to Improve the Traveler Experience. In J.V. Carvalho, A. Rocha, P. Liberato, & A. Peña (Eds.), Advances in Tourism, Technology and Systems 2023. [CrossRef]
Hassan, M.A.E.; Zerva, K.; Aulet, S. A Model for Brand Personality Word Embedding: Identifying UNESCO World Heritage Personality Categories. Journal of Travel Research 2024, 63(1), 44–63. [CrossRef]
Šormaz, A., Ruoss, E. Social Media as a Management Tool: Opportunities for Sustainable Heritage Destinations. Academica Turistica 2023, 16(1), 121–138. [CrossRef]
Hassan, M.A.E.; Zerva, K.; Aulet, S. Brand Personality Traits of World Heritage Sites: Text Mining Approach. Sustainability 2021, 13(6). [CrossRef]
Koufodontis, N.I.; Gaki, E. UNESCO urban world heritage sites: Tourists’ awareness in the era of social media. Cities 2022, 127(103744). [CrossRef]
Peira, G.; Beltramo, R.; Pairotti, M.B.; Bonadonna, A. Foodservice in a UNESCO Site: The Restaurateurs’ Perception on Communication and Promotion Tools. Sustainability 2018, 10, 2911. [CrossRef]
Rastegar, R.; Zarezadeh, Z. Millennials and social media marketing: the case of Indian UNESCO world cultural heritage sites. In K. W. Sandeep, & Jasrotia, A. (Eds.), Millennials, spirituality and tourism 2022, Routledge, pp. 157-178, ISBN: 9781003170631.
Konstantakis, M.; Christodoulou, Y.; Alexandridis, G.; Teneketzis, A.; Caridakis, G. ACUX Typology: A Harmonisation of Cultural-Visitor Typologies for Multi-Profile Classification, Digital 2022, 2, 365–378. [CrossRef]
Olorunsola, V.O.; Saydam; M.B., Lasisi, T.T.; Ozturen, A. Exploring tourists’ experiences when visiting Petra archaeological heritage site: voices from TripAdvisor. Consumer Behavior in Tourism and Hospitality, 2023, 18(1), 81–96. [CrossRef]
Rohmer, J.; Charloux, G. From Liḥyān to the Nabataeans: dating the end of the Iron Age in north-west Arabia. Proceedings of the Seminar for Arabian Studies, Vol. 45, Papers from the forty-eighth meeting of the Seminar for Arabian Studies held at the British Museum, London, 25 to 27 July 2014, pp. 297–319, ID: ⟨halshs-02169599⟩.
Al-Ansary, A. The State of Lihyan: A New Perspective. Topoi 1999, 9(1). [CrossRef]
Nehmé, L. Explorations récentes et nouvelles pistes de recherche dans l’ancienne Hégra des Nabatéens, moderne Al-Hijr/Madā'in Sālih (Arabie du Nord-Ouest). Comptes rendus des séances de l’Académie des Inscriptions et Belles- Lettres 2004, 148(2).
Fiema, Z.T.; Villeneuve, F.; Bauzou, T. New Latin and Greek Inscriptions from Ancient Hegra. Zeitschrift Für Papyrologie Und Epigraphik 2020, 214, 179–202.
Charloux, G.; Bouchaud, C.; Durand, C.; Garber, Y.; Studer, J. Living in Madāʾin Ṣāliḥ/Hegra during the late pre-Islamic period. The excavations of Area 1 in the ancient city. Proceedings of the Seminar for Arabian Studies, Vol. 48, Papers from the fifty-first meeting of the Seminar for Arabian Studies held at the British Museum, London, 4th to 6th August 2017, pp. pp. 47–65, ISBN: 1784918776.
UNESCO. Hegra Archaeological Site, 2024, available at: https://whc.unesco.org/en/list/1293/ (consulted 14 January 2024).
Villeneuve F.; Al-Talhi D.; Nehmé L. Résultats préliminaires de la première campagne de fouille à Madâ’in Sâlih en Arabie Saoudite. In Comptes rendus des séances de l'Académie des Inscriptions et Belles-Lettres 2008, 152e année, N. 2, 651–691.
Oxford Archaeology. Al Ula Archaeological Survey, 2018, available at: https://oxfordarchaeology.com/news/796-al-ula-archaeological-survey (consulted 16 January 2023).
Kingdom of Saudi Arabia. Saudi Vision 2030, 2024, available at: https://www.vision2030.gov.sa/v2030/overview/ (consulted 15 January 2024).
AlrawAlrawaibah, A. Archaeological Site Management in the Kingdom of Saudi Arabia: Protection or Isolation? In K. Exell & T. Rico (Eds.), Cultural Heritage in the Arabian Peninsula, 2016, Routledge, pp. 143–156, ISBN: 9781315575285.
Alqahtany, A.; Aravindakshan, S. Urbanization in Saudi Arabia and sustainability challenges of cities and heritage sites: heuristical insights. Journal of Cultural Heritage Management and Sustainable Development 2022, 12(4), 408–425. [CrossRef]
Manning, C.D.; Raghavan, P.; Schütze, H. Boolean retrieval. Introduction to Information Retrieval 2008, 1–18.
Tripathy, A.; Agrawal, A.; Rath, S.K. Classification of sentiment reviews using n-gram machine learning approach. Expert Systems with Applications 2016, 57, 117–126. [CrossRef]
Scott, J. Social network analysis: Developments, advances, and prospects. Social Network Analysis and Mining, 2011, 1, 21–26. [CrossRef]
Murtagh, F.; Legendre, P. Ward’s hierarchical agglomerative clustering method: Which algorithms implement Ward’s criterion? Journal of Classification 2014, 31, 274–295.
Lee, A.; Willcox, B. Minkowski generalizations of Ward’s method in hierarchical clustering. Journal of Classification 2014, 31, 194–218. [CrossRef]
Kapil, S.; Chawla, M. Performance evaluation of K-means clustering algorithm with various distance metrics 2016, 1–4.
Liu, B., Zhang, L.A survey of opinion mining and sentiment analysis. In C. C. Aggarwal, & C. X. Zhai (Eds.), Mining text data 2012, Springer, pp. 415–463, ISBN: 978-1-4614-3222-7.
Tang, D.; Wei, F.; Qin, B.; Yang, N.; Liu, T.; Zhou, M. Sentiment embeddings with applications to sentiment analysis. IEEE Transactions on Knowledge and Data Engineering 2015, 28(2), 496–509. [CrossRef]
Kirilenko, A.P.; Stepchenkova, S.O.; Kim, H.; Li, X. Automated sentiment analysis in tourism: Comparison of approaches. Journal of Travel Research 2018, 57(8), 1012–1025. [CrossRef]
Liu, S. M.; Chen, J.H. A multi-label classification based approach for sentiment classification. Expert Systems with Applications 2015, 42(3), 1083–1093. [CrossRef]
Borg, A.; Boldt, M. Using VADER sentiment and SVM for predicting customer response sentiment. Expert Systems with Applications 2020, 162, 113746. [CrossRef]
Hutto, C.; Gilbert, E. Vader: A parsimonious rule-based model for sentiment analysis of social media text 2014, 8(1), 216–225. [CrossRef]
Bonta, V.; Kumaresh, N.; Janardhan, N. A comprehensive study on lexicon based approaches for sentiment analysis. Asian Journal of Computer Science and Technology 2019, 8(S2), 1–6. [CrossRef]
Hutto, C. VADER-Sentiment-Analysis, GitHub, 2020.
Pano, T.; Kashef, R.A complete VADER-based sentiment analysis of bitcoin (BTC) tweets during the era of COVID-19. Big Data and Cognitive Computing 2020, 4(4), 33. [CrossRef]
Qiang, J.; Li, Y.; Yuan, Y.; Liu, W. Snapshot ensembles of non-negative matrix factorization for stability of topic modeling. Applied Intelligence 2018, 48, 3963–3975. [CrossRef]
Arora, S.; Ge, R.; Moitra, A. Learning topic model. Going beyond SVD 2012, 1–10.
Kuang, D.; Choo, J.; Park, H. Nonnegative matrix factorization for interactive topic modeling and document clustering. Partitional Clustering Algorithms 2015, 215–243.
Lee, D.D.; & Seung, H.S. Learning the parts of objects by non-negative matrix factorization. Nature 1999, 401(6755), 788–791. [CrossRef]
Xu, Q.; Zhu, L.; Dai, T.; Guo, L.; Cao, S. Non-negative matrix factorization for implicit aspect identification. Journal of Ambient Intelligence and Humanized Computing 2020, 11, 2683–2699. [CrossRef]
Schmidt, M.N.; Winther, O.; Hansen, L.K. Bayesian non-negative matrix factorization 2009, 540–547.
Lee, A.; Willcox, B. Minkowski generalizations of Ward’s method in hierarchical clustering. Journal of Classification 2014, 31, 194–218. [CrossRef]
Paatero, P. Least squares formulation of robust non-negative factor analysis. Chemometrics and Intelligent Laboratory Systems 1997, 37(1), 23–35. [CrossRef]
Kuang, D.; Brantingham, P.J.; Bertozzi, A.L. Crime topic modeling. Crime Science 2017, 6(1), 1–20.
Visit Rwanda. Gorilla Tracking, 2024, available at: https://www.visitrwanda.com/interests/gorilla-tracking/#:~:text=Book%20Your%20Gorilla%20Tracking%20Experience&text=Cost%20US%20%241500%20per%20person,and%20East%20African%20Community%20residents. (consulted 15 January 2024).
Machu Pichu. Reservas, 2024, available at: https://reservas.machupicchu.gob.pe/inicio (consulted 15 January 2024).
Visit Ghana. Kakun National Park, 2024, vailable at: https://visitghana.com/attractions/kakum-national-park/ (consulted 15 January 2024).
Arizona State Park. Archeological Site Etiquette, 2024, available at: https://azstateparks.com/archaeological-site-etiquette (consulted 15 January 2024).
Parco Archeologico del Colosseo. Parco Archeologico del Colosseo, firmato il contratto con il nuovo gestore della biglietteria, 2024, vailable at: https://colosseo.it/2023/11/colosseo-nuovo-gestore-biglietteria/ (consulted 15 January 2024).
Pompei. Crcuito Archeologico Vesuviano, 2024, available at: https://www.ticketone.it/artist/circuito-archeologico-pompei/ (consulted 15 January 2024).

Figure 1. Compound score distribution assessment.

Figure 2. Topic discovery: non-negative matrix factorization by Kuang et al. (2017).

Figure 3. Count of reviews.

Figure 4. Count of reviews by rating.

Figure 5. Distribution of review text length by rating.

Figure 6. Visitor nationalities of Hegra site (2010-2023).

Figure 7. Top 30 most frequent bigrams and their dendrogrammatic co-occurrence matrix.

Figure 8. Top 30 most frequent trigrams and their dendrogrammatic co-occurrence matrix.

Figure 9. Topic modeling for positive reviews.

Figure 10. Topic modeling of negative reviews.

Figure 11. Topic modeling of neutral reviews.

Table 1. Descriptive statistics for review sentiment scores.

	Count	Mean	std	min	25%	50%	74%	max
Negative	21	-0.290914	0.194548	-0.7825	0.4215	-0.2551	-0.1531	-0.0516
Neutral	11	0.012318	0.018190	0.0000	0.0000	0.0000	0.0249	0.0485
Positive	213	0.769665	0.231443	0.0516	0.6808	0.8555	0.9360	0.9962

Table 2. Analysis of reviews by rating.

Review Rating	Count	Mean	Std	min	25%	50%	75%	Max
1 (Very Poor)	8	267.875000	160.450649	104.0	162.5	221.5	335.25	583.0
2 (Poor)	4	702.500000	724.749382	119.0	215.0	487.5	975.00	1716.0
3 (Average)	11	287.454545	156.782246	71.0	146.5	325.0	414.00	535.0
4 (Very Good )	47	379.893617	454.262002	67.0	175.0	262.0	390.50	2742.0
5 (Excellent)	175	345.388571	352.828176	55.0	139.0	237.0	422.50	2988.0

Table 3. Central themes in positive reviews.

Topic	Thematization	Examples of reviews
1	Unicity of the site	“Second only to Petra, this former capital of Nabataeans Empire, is a must visit site on your trip to Al Ula” (1)
2	Importance of the travel experience	“We really enjoyed the tour. Everything is very organized” (18) “It’s a very organized bus tour around the site (16)
3	Perceived beauty of the place	“The whole place is amazing and really worth visiting” (2) “Outstanding place” (8)
4	Relevance of the historical background	“A must see for people looking to immerse themselves in the culture and history of Nabatean” (26) “You travel through time and history and imagine how those people lived and what they did” (74)
5	Accessibility to the place	“We were also quite surprised that they have complete rebuild the 100 year old railway carriages… It was the location where Laurence of Arabia started his second attack against the Turks” (85)
6	Wide range of attractions	“The site is spectacular both for its tombs carved into the face of the rock and its desert setting” (3) “The tombs and temples were quite amazing and beautiful” (47)

Table 4. Central themes in negative reviews.

Topic	Thematization	Examples of reviews
1	Isolation	“The site is so far away as Saudi Arabia is such a big country so that a school trip would be out of the question” (5) “After travelling a long long distance from taif to there we were returned back from the gate” (49)
2	High price	“The costs have increase by almost 3 times for the same trip” (60) “cost 250 SAR which is very expensive especially when there was no child price ticket” (5).
3	Privatization	“It is mandatory to book the visit to Hegra through "Experience AlUla"” (13)
4	Competitiveness	“If you are just a normal tourist wanting to see something new then visit Petra, as it is much more impressive and Jordan is more welcoming to tourists with much better facilities” (99)

Table 5. Central themes in neutral reviews.

Topic	Thematization	Examples of reviews
1	The importance of the place	“This place is show you the culture of past centuries society, You never stop your self to encourage the Art which was made on stone. To understand about old living standard you must study about it” (134)
2	The features of the tombs	“Egistered World Heritage site. More than 100 tomb of Nabataean civilization extension of the Nabataeans in Petra. The most important sites where the ِAlfared Palace and the Albaint Palace of and the Asanee Palace” (152)

Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.

© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

Copyright: This open access article is published under a Creative Commons CC BY 4.0 license, which permit the free download, distribution, and reuse, provided that the author and preprint are cited in any reuse.

MDPI Initiatives

Important Links

Choose an area of interest and we will send you notifications of new preprints at your preferred frequency.

Disclaimer

Withdrawn:

Tourism Development through the Sense of UNESCO World Heritage: The Case of Hegra, Saudi Arabia

Abstract