Tourist Reviews Research Articles

OACTD: an ontology-embedded model for heritage tourism reviews to assign cultural tourism data

ABSTRACT The rapid growth of cultural tourism, driven by the ‘heritage boom’, has brought about the urgent need to reduce the cost and broaden the audience of cultural tourism. To address this challenge, this paper proposes OACTD, an ontology-embedded model for heritage tourism reviews to assign cultural tourism data. The OACTD model integrates scattered and unstructured tourist reviews, making them directly beneficial to cultural tourists and related management organizations, while also reducing the costs of tourism. The model first integrates large volumes of tourist reviews from various popular tourism platforms, and then utilizes natural language processing (NLP) algorithms to extract key information. This information is subsequently structured and visualized through an ontology model, which is further enhanced based on tourist queries. To demonstrate the effectiveness of the model, a case study was conducted on the Xiangjiang Ancient Town Group, involving the extraction of key information and the construction of an ontology from 8016 tourist reviews. This study presents a novel approach to leveraging cultural tourism data, promoting the formation of a two-way ‘demand-supply’ digital cultural tourism ecological chain.

Exploring Tourist Experience through Online Reviews Using Aspect-Based Sentiment Analysis with Zero-Shot Learning for Hospitality Service Enhancement

Hospitality services play a crucial role in shaping tourist satisfaction and revisiting intention toward destinations. Traditional feedback methods like surveys often fail to capture the nuanced and real-time experiences of tourists. Digital platforms such as TripAdvisor, Yelp, and Google Reviews provide a rich source of user-generated content, but the sheer volume of reviews makes manual analysis impractical. This study proposes integrating aspect-based sentiment analysis with zero-shot learning to analyze online tourist reviews effectively without requiring extensive annotated datasets. Using pretrained models like RoBERTa, the research framework involves keyword extraction, sentence segment detection, aspect construction, and sentiment polarity measurement. The dataset, sourced from TripAdvisor reviews of attractions, hotels, and restaurants in Central Java, Indonesia, underwent preprocessing to ensure suitability for analysis. The results highlight the importance of aspects such as food, accommodation, and cultural experiences in tourist satisfaction. The findings indicate a need for continuous service improvement to meet evolving tourist expectations, demonstrating the potential of advanced natural language processing techniques in enhancing hospitality services and customer satisfaction.

Sentiment Analysis of Reviews of Tourist Attractions in the Lake Toba Area Using the Naïve Bayes Method

Lake Toba is one of the tourism destinations in Indonesia which is the main destination for domestic and foreign tourists. However, the natural beauty of Lake Toba is not enough to develop quality tourist destinations, so analysis needs to be carried out in order to develop tourist destinations that suit tourist needs with the aim of improving the economy from tourism, especially at Lake Toba. One aspect that must be analyzed is comments from tourists who have visited Lake Toba via various platforms. This is very influential for potential future tourists to have a reference for Lake Toba tourism. The analysis process can be carried out by analyzing comments using the Naive Bayes method so that managers of the Lake Toba tourist destination can improve tourist attractions and provide tourist satisfaction and develop various tourism innovations to meet various tourist needs in a sustainable manner. The results of the sentiment analysis of Lake Toba tourist reviews using Naive Bayes detected 31 positive labels, 378 neutral labels and 7 negative labels with an accuracy result of 77.49% from 1260 data, where training data was 1008 and test data was 252 data.

Exploring the decision model for tourism symbiosis of ancient villages and towns based on tourist reviews: A case study of Huaihua City in China

AbstractTo ensure the sustainable development of regional tourism, it is essential to differentiate between adjacent tourist attractions in a state of homogeneous competition. This study proposes a decision model for the tourism symbiosis of ancient villages and towns (AVTs) using tourist reviews. Their co‐occurrence and individual characteristics were extracted through content analysis, and a probabilistic linguistic CODAS method with a newly defined distance was employed for sentiment analysis. Four clustered AVTs located in Huaihua City, China, were selected to validate the decision model. The results indicate that a long history and culture, ethnic customs, and a serene atmosphere comprise co‐occurrence characteristics, while material landscape, spiritual culture, emotional cognition, and tourism services comprise individual characteristics. The corresponding tourism symbiotic development schemes, including cultural construction, life experience, and pastoral entertainment, are proposed based on the advantages and potentials perceived by tourists. The decision model could contribute to sustainable development of rural areas.

Sentiment analysis applied to tourism: exploring tourist-generated content in the case of a wellness tourism destination

ABSTRACT Sentiment analysis of content generated by tourists through social media platforms can be used to generate knowledge about a wellness tourism destination. The present research aims to explore the content generated by tourist reviews, analyse the sentiments about a wellness tourism destination made in comments on hotels, evaluate the sentiment of tourists towards the various components of this kind of destination, and identify the motivations for tourists to look for wellness tourism destinations, such as the Algarve. The analysis of the reviews of the tourism components demonstrates tourists’ opinions about the wellness destination and also grades the infrastructure facilities and the highlighted services for the categories of this type of tourism. In attaining the objectives of this study, quantitative methods were used to review 1294 comments extracted from TripAdvisor, to which text mining algorithms were applied to assess the sentiment expressed in the comments. The results show that the three keywords most frequently used for all component categories in the Algarve wellness destinations are “great”, “love” and “visit”, and also reveal that a hotel providing spa and massage services in the tourist wellness destination mainly satisfies the desires of a tourist.

Content Analysis of Brand Awareness Rural Tourism through Instagram Reviews

Tourism trends have shifted. The Covid-19 pandemic has caused a change from mass tourism towards alternative tourism that prioritizes nature and authentic culture, one of which is rural tourism. This research aims to determine the form of content, content characteristics, message effectiveness, and level of brand awareness of Pujon Kidul Rural tourism contained in tourist uploads on Instagram social media. This research design is exploratory with qualitative and quantitative content analysis methods. Data was obtained from the Instagram accounts of tourists who reviewed Pujon Kidul Rural tourism. The sample selection technique used in this research was purposive sampling of 200 posts. Quantitative data was processed using SPSS 25.0 and qualitative data was processed using Nvivo 12. The results showed that the most common form of content is images, personal Instagram accounts are more dominant in conducting reviews and use non-standard language, Instagram review messages are interesting, have the potential to raise awareness for holidays, and increase brand awareness of rural tourism. Tourist reviews are at the brand recognition level. Rural tourism managers must create product differentiation and make maximum use of social media platforms so that Pujon Kidul rural tourism is at the top-of-mind level. Keywords: brand awareness, content analysis, Instagram, rural tourism, social marketing

Sharing travel experiences on TripAdvisor: A genre analysis of negative hotel reviews written in French, Spanish and Italian

The advent of the Web 2.0 brought about a significant transformation in the tourist experience and in its modes of communication. One of the prevailing genres in today's digital tourism discourse is represented by online reviews, produced and consumed daily by millions of users on global platforms such as TripAdvisor, Booking.com or Airbnb. Online tourist reviews are mainly used to share a personal (travel) experience with service providers and fellow travelers, at the same time they offer an evaluation of tourism services.The aim of the present case study is to provide an in-depth investigation of how tourists are sharing their post-trip experiences online from a linguistic and pragmatic standpoint. Specifically, we focused on negative hotel reviews posted on TripAdvisor and we adopted a cross-linguistic perspective. Building on previous work, we now focused on a different set of languages, considering reviews written in Spanish, Italian and French (N100 for each language, for a total of N300) posted between 1st of January 2021 and 31st December 2022. Ultimately, the aim was to verify whether users writing in different languages share their experiences adopting uniform linguistic norms and communicative habits or display different discursive preferences. In order to reach this goal, we analyzed not only which topics tourists include in their reviews, but also which communicative moves they adopted to share their experiences and opinions. Results indicate a tendency towards a cross-linguistic similarity. This finding seems to point toward a potential standardization process of how travel experiences are shared online in different languages, within the highly popular genre of online reviews.

IDENTIFYING AMERICAN TOURISTS’ UNIQUE EXPERIENCES FROM THE LINCOLN MEMORIAL

Detailed experiences of travelers are presented in online tourist reviews that affect the way other tourists perceiveand plan their trips. Such reviews are sources of information in the form of open writing that allows reliable sharing of experienceabout tourist attractions. Previous studies have made use of tourist reviews to obtain lists of the most well-known tourist destinationsand their attributes or to evaluate service in the hospitality industry. This study looks at tourist reviews from a new perspective andidentifies unique tourism experiences using a combination of algebraic modeling and sentiment analysis applied to reviews about theLincoln Memorial. Our research findings reveal that they are based on neutral and negative assessments of the trips and refer to intangible items. Overall, unique tourism experiences can give useful advice on how to make a trip to the memorial comfortable and enjoyable. Additionally, they could provide travel managers with reliable information about the attraction for effective marketing campaigns.

Understanding Fine-Grained Sentiments of Super-Priority Destination Visitors using Multi-task Learning for Extraction of Aspect Terms and Polarity Classification on Reviews

One sector that plays an important role in the national economy is the Tourism sector. To maintain economic stability, the Indonesian Government has made various efforts to mitigate the impact of the Covid-19 pandemic on the tourism sector. The National Economic Recovery (PEN) is one of its programs, specifically focused on the recovery of tourism, particularly in the 5 super-priority destinations (Lake Toba, Borobudur, Mandalika, Labuan Bajo, Likupang). To ensure the effective recovery of these 5 super-priority destinations, in addition to information from stakeholders, the government can conduct studies based on the aspirations of the people. One task that can assist the government in summarizing user reviews is sentiment analysis. Previous studies have focused on comments from the local Indonesian population, which have not reached the majority of tourists who comment in English. Therefore, there is a need for Sentiment Analysis (SA) at the aspect level on TripAdvisor reviews of the Super Priority Destinations (SPDs) using the English language. The method used for sentiment analysis of these tourist reviews is the Local Context Focus - Aspect Term Extraction & Aspect Polarization Classification (LCF-ATEPC). This method employs multi-task learning, domain Bidirectional Encoder Representations from Transformers (BERT), and information about the aspect position within a sentence. Based on the research, the LCF-ATEPC model has proven to be capable of performing sentiment analysis of English aspects effectively. The best-performing LCF-ATEPC model was obtained using a dataset without outliers (95%) with APC-ACC, APC-F1, and ATE-F1 accuracies of 83.33%, 69.5%, and 66.83%, respectively.

Sustainable Urban Planning and Genius Loci: A LDA-Based Analysis of Recreational Landscape Imagery in Hangzhou’s Qiaoxi Historic District

Amidst the impacts of urbanization and commercialization, the cultural and historical essence of neighborhoods is undermined, paralleled by the diminishing Genius Loci. This research explores the previously under-researched area of how urban leisure districts’ spatial design influences tourists’ experiential imagery perceptions and the Genius Loci, aiming to elucidate their dialectical relationship. Leveraging LDA topic modeling and TF-IDF text analysis, we distill behavioral and cognitive imageries from tourist reviews to forge a perception framework of the Genius Loci, bifurcated into spiritual and directional imagery dimensions. Secondary dimensions emerge through TF-IDF keyword extraction. GIS technology further analyzes the spatial distribution of emotional imageries and the spatiotemporal dynamics of emotions, highlighting its significance in sustainable urban development. The findings reveal the multidimensional experiential imageries of tourists in leisure neighborhoods and their impact and feedback on the Genius Loci, offering a new perspective on the interaction between tourists’ emotional spatial experiences and the Genius Loci. It is discovered that tourist perceptions mainly concentrate on “environmental psychological perception dimension” and “spatial orientation perception dimension” with the spatial distribution of tourists’ emotional experiences showing significant temporal dynamics. These results underline a complex interaction between tourists’ experiential imagery and the Genius Loci, suggesting that tourists’ feedback could play a critical role in shaping the evolution of these cultural spaces, emphasizing the need for integrated approaches in urban planning and heritage conservation.

Fun or Fear? On-Site Versus On-Screen Experiences in Paranormal Tourism: A Case Study of Hashima Island, Japan

Paranormal tourism has seen increased demand, particularly from those tourists who seek unusual or unique experiences. Various paranormal phenomena offer multidimensional experiences for tourists in different physical contexts. Similarly, the advancement of technology has extended the ability to offer more authentic on-screen paranormal experiences. This study aims to explore and compare tourists’ on-site and on-screen experiences at Hashima Island, Japan, with an interest in identifying paranormal-related emotions. Tourist reviews and comments from Tripadvisor and YouTube were collected, with data analysis conducted using grounded theory and Leximancer. Key findings indicate that besides core paranormal encounters, some inviolate features also contribute to paranormal experiences, with tour guide services impacting the on-site experience and video filming techniques influencing the on-screen experience. Furthermore, this work revealed several emotional dimensions, with “fear” and “surprise” positively related to paranormal experiences. By comparing on-site and on-screen experiences, this study offers marketing and planning suggestions for destinations seeking to develop and promote paranormal tourism encounters.

Pengaruh Citra Destinasi Dan Atribut Destinasi Terhadap Minat Berkunjung Ulang Wisatawan Ke Destinasi Wisata Alahan Panjang

This research was motivated by the intention phenomenon to revisit the Alahan Panjang destination. The length is influenced by the tourist destination image and the attributes of the tourist destination in Alahan Panjang. Several tourist reviews state that the tourist destination Alahan Panjang is not Recommended for those who re-visit and haven't experienced the attraction yet impressive for tourists. This research aims to analyze the tourist destination image and attributes of the Alahan Panjang tourist destination. In this research using quantitative methods, the research type used in this research is causal associative or cause-effect relationships. Data was obtained from survey results by distributing questionnaires to respondents which is the object of research. The average for the tourist destination image variable is 4.112 with criteria Good. The tourist destination attribute variable has an average of 3.996 with Good criteria. Then The tourist destination attribute variable has an average of 4.016 with Good criteria. So therefore There is a positive and significant influence between the image of a tourist destination and interest in visiting repeated the tourist. There is also an influence between tourist destination attributes and interest in visiting repeated the tourist. Destination image variables and tourist destination attributes have an impact on variable interest in revisiting, then H1, H2 and H3 are accepted.

Mining for tourists in China: a digital ethnography of user-generated content from coal mining heritage parks

ABSTRACT Mine parks and industrial heritage are relatively recent tourism phenomena, emerging in Europe and North America during the mid-twentieth century. In the People’s Republic of China, government officials undertook large-scale promotion of mining heritage between 2005 and 2021, when 88 former state-sector mines were designated national parks. From an official vantage point, transforming former extractive industries into heritage sites helped communities negatively affected by the social and environmental legacies of mining and mine closure to pursue a future in China’s lucrative tourism sector. To date, this endeavour has been little studied, with research on visitors’ experiences particularly limited. In this article, we interrogate Chinese tourists’ responses to national mining heritage by analysing online user-generated content (tourist reviews) from three coal-mines-turned-heritage parks. We ask how visitors made meaning at these sites, and whether and how the mining tourism imaginaries they co-constructed in online reviews resembled visitor experiences of mining heritage elsewhere. How Chinese tourists respond to the parks not only affects the state’s ability to achieve its development goals, but also informs perceptions of mining, energy production, and tourism more broadly. Such perceptions have implications for sustainable resource and energy use.

성읍민속마을의 관광객 평가에 대한 네트워크 분석과 감성분석 -트립어드바이저 리뷰를 중심으로-

In this study, network analysis and sentiment analysis were performed to understand
 tourists' evaluation of Seongeup Folk Village through review analysis of TripAdvisor, a
 travel platform. As a result of network analysis, tourists who selected 1 or 2 stars
 wrote reviews focusing on forcing, selling, and soliciting products sold in Seongeup Folk
 Village, and tourists who selected 3 or 4 stars wrote reviews focusing on sales of
 products and local guides. Tourists who selected 5 stars wrote reviews focusing on the
 contents of the local guide. As a result of sentiment analysis, the higher the star rating,
 the higher the frequency of using positive words about Seongeup Folk Village. On the
 contrary, among the negative words, the words of the rejection category were used
 frequently, and in detail, 'terrible', 'bad', and 'uninteresting' appeared with high frequency.
 Through the analysis of tourist reviews, the strength of Seongeup Folk Village is the
 local guide system, and the weakness is sales of products and solicitation. Therefore,
 this study recognizes these points in the future, maximizes strengths, and minimizes
 weaknesses. It is considered that it can be used as basic data.

Use and Experience of Tourism Green Spaces in Ishigaki City before and during the COVID-19 Pandemic Based on Web Review Data

Urban Tourism Green Spaces (UTGSs) in Okinawa are the most frequented areas by tourists and are an important part of the area’s unique tourism image and brand value. This study selected 40 UTGSs in Ishigaki City, Okinawa, for comparative analysis before the COVID-19 pandemic (January 2009–December 2019) and during the strict control period (January 2020–December 2021). Data on tourist reviews were collected in Python. Correlations were collated through Pearson correlation tests. Text-mining methods were used to examine tourist reviews to characterise changes in tourists’ use and experience of UTGSs amid the extreme hardship experienced by tourist cities during the pandemic. Visitors expressed positive emotions towards the green space, particularly in post-pandemic times, despite disruptions and uncertainties caused by travel restrictions. Rather than focusing on water activities as before, visitors preferred exploring the island’s hinterland areas with low crowd density and seeking spiritual comfort in open public spaces. This highlights the potential of these areas to be developed as a new tourism resource. This study’s findings will provide valuable insights for urban planners and tourism operators in developing effective crisis response strategies and guiding the development of urban tourism areas towards more appealing and sustainable destinations.

A New Approach to Extracting Tourism Focus Points from Chinese Inbound Tourist Reviews after COVID-19

The number of inbound tourists in Japan has been increasing steadily in recent years. However, due to the COVID-19 pandemic, the number of inbound tourists decreased in 2020. This is particularly worrisome for Japan, as the number of inbound tourists is expected to reach 60 million per year by 2030. In order to help Japan’s tourism industry to recover from the pandemic, we propose a method of identifying elements that attract the attention of inbound tourists (focus points) by analyzing reviews on tourist sites. We focus on Hokkaido, a popular area in Japan for tourists from China. Our proposed method extracts high-frequency n-gram patterns from reviews written by Chinese inbound tourists, showing which aspects are mentioned most often. We then use seven types of motivational factors for tourists and principal component analysis to quantify the focus points of each tourist destination. Finally, we estimate the focus points by clustering the n-gram patterns extracted from the tourists’ reviews. The results show that our method successfully identifies the features and focus points of each tourist spot.

Co-creation of food tourism experiences: Tourists’ perspectives of a Lisbon food tour

Gastronomy has become a distinctive tourism product with the potential of contributing to visitors’ engaged immersion in destinations. Few studies have reflected on visitors’ perceptions of participation in food tour experiences, and research on co-creation in food tours is even more scarce. This study analyses co-creative tourism experience dimensions, previously identified in the literature, through a passive netnography, involving 658 online tourist reviews on TripAdvisor of a food tour experience in Lisbon, to understand how value was co-created by comparing the perceptions of visitors and the service provider. Interview-based data were subject to content analysis. Results showed that “aesthetics/sense,” “feel,” “thinking/education,” “relate,” and “personalization” were the most reported dimensions in tourists’ discourse, which was confirmed by the service provider. The present study provides insights to destination management organizations, marketers and entrepreneurs in creating opportunities for and designing appealing co-creative food experiences.

Exploring temporal and spatial structure of tourism market through a big data approach: Whether geographic distance still matters?

The prevalence of tourism social media data offers a new approach to examining the tourism market structure beyond traditional methods. This paper aims to explore the spatiotemporal structure of the tourism market. A total of 75,295 tourist reviews of Sanya, a famous tropical coastal destination in China, were collected from five representative Chinese online travel agencies. Assisted by the innovative techniques of sentiment analysis and regression analysis, this paper examines the relationship between geographic and economic distance and the tourism market structure of Sanya city, represented by the tourists’ online attention and reputation. The findings show that the socioeconomic factors of the origins had a greater impact on the online attention of a destination. This study sheds light on how big data can be applied to verify traditional theories in tourism geography.

Analisis Sentimen Wisatawan terhadap Taman Nasional Bunaken dan Top 10 Hotel Rekomendasi Tripadvisor Menggunakan Algoritma SVM dan DT berbasis CRISP-DM

It is necessary to analyze traveler sentiment towards Bunaken National Park and Tripadvisor's Top 10 Recommended Hotels to identify traveler satisfaction with the attractions, accommodation services, and transportation used. Considering this, this study uses the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework by testing the performance of the Decision Tree (DT) algorithm and the Support Vector Machine (SVM). CRISP-DM has six stages: business understanding, data understanding, data preparation, modeling, evaluation, and deployment. Based on the processing of 398 Bunaken National Park destination data and 1793 review data on the top 10 hotels recommended by Tripadvisor, the SVM algorithm performed better. In the context of Bunaken National Park destination data, the performance of the SVM algorithm using the SMOTE operator can produce 100% accuracy, precision, recall, f-measure, AUC, and t-Test values. In addition, in processing the top 10 hotel datasets recommended by Tripadvisor, the SVM algorithm using the SMOTE operator provides an accuracy value of 96.42%, a precision value of 100%, a recall value of 92.83%, an f-measure value of 96.27%, an AUC value of 100%, and a t-Test value of 96.4%. The results of identifying the five words that most often appear in tourist reviews for Bunaken Marine Park tourist destinations are 164 words fish, 165 words island, 193-word dive, 230 diving, and 280 words Bunaken. This indicates the driving factor for tourist visits to Bunaken Marine Park is the beauty of underwater nature, including the diversity of marine animal species, the natural beauty of the archipelago, and diving activities. In addition, the results of identifying the five words that most often appear in traveler reviews for Tripadvisor's top 10 recommended hotels are 1170 great words, 1222 bunaken words, 1550 resort words, 1613 diving words, and 1711 dive words. This indicates that the characteristics of tourists who have the motive of traveling to Bunaken National Tourism Park have the intention to use resort accommodations around Bunaken and have mobility and facilities to support diving activities. Thus, the output of this study can be used as a recommendation for accommodation service managers to prepare supporting facilities for tourists who want to visit Bunaken National Park.

Tour guides’ self-efficacy and resilience capability building through sharing economy platforms

PurposeThis study aims to explore how the local tour guides (LTGs) operate through the sharing economy platform. This study explores how LTGs have responded to the COVID-19 pandemic restrictions using self-efficacy and other resources to improve resilience and performance. This study also delineates the working mechanisms of peer-to-peer (P2P) platform-enabled, dynamic capability building processes, in the tourism sharing economy.Design/methodology/approachThis research adopted an interpretive approach to understand the focal phenomenon using two types of data. A total of 40 semi-structured interviews with LTGs and 26,478 online tourist reviews from tour guide service participants’ before and during the COVID-19 pandemic were used.FindingsThe findings of this study revealed that LTGs used sharing economy platforms to arrange flexible tour guide services. Resilience emerged through dynamic capability that addressed contextual factors in real time. LTGs coordinated different resources and customers during a time of uncertainty. Different sources of self-efficacy and types of dynamic capability were identified. The interplay between LTGs’ self-efficacy and dynamic capability was also delineated.Practical implicationsThe findings provide guidance for LTGs on P2P platforms and other sharing economy sectors on how diverse resources enabled by the sharing economy can enhance resilience during times of uncertainty. LTGs that engage with contextual information and are dynamic can adopt itineraries and services that will benefit tourists and their business.Originality/valueThis study contributes to the sharing economy literature by theorizing the working flow that enables LTGs to exert self-efficacy and leverage dynamic capability on P2P platforms. This study also contributes by linking resilience to contextual factors in real time. The outcomes provide guidance for LTGs to remain competitive and establish resilience in uncertain environments.