Search Results (86)

The sustainable development of traditional villages faces a core challenge stemming from the disconnect between public perception and spatial planning. To address this issue, this study, taking Baishe Village—a national-level traditional village—as a case study, constructs and applies a “Digital Humanities + Spatial Analysis” research paradigm that integrates text mining, sentiment analysis, visual coding, and spatial analysis based on multimodal social media data (Sina Weibo and Xiaohongshu) from 2013 to 2023. It aims to conduct an in-depth analysis of tourists’ rural image perception structure, emotional tendencies, and their spatial differentiation characteristics, and subsequently propose spatial optimization pathways that promote the revitalization of its cultural landscape and sustainable land use. The main findings reveal the following: (1) In terms of cognitive structure, the rural image presents a ‘settlement-dominated’ four-dimensional structure, with settlement elements such as pit kilns (accounting for more than 70%) as the absolute core. (2) In terms of emotional tendencies, a cognitive tension is formed between the high recognition of architectural heritage value (positive sentiment: 57.44%) and significant dissatisfaction with service facilities. (3) In terms of spatial patterns, a “dual-core-driven” pattern of perceived hotspots emerges, with 83% of tourist activities concentrated in the central–southern main road area, revealing a “revitalization gap” in village spatial utilization. The contribution of this study lies in translating abstract public perceptions into quantifiable spatial insights, thereby constructing and validating a “Digital Humanities + Spatial Analysis” paradigm that fuses multimodal data and links abstract perception with concrete space. This provides a crucial theoretical basis and practical guidance for the living conservation of cultural landscapes, the enhancement of land use efficiency, and refined spatial governance. Full article

This study examines how hotel characteristics and urban spatial context influence tourist satisfaction in Seoul, South Korea, by integrating sentiment analysis of online reviews with regression modeling. Drawing on 4500 TripAdvisor reviews from 75 hotels, sentiment scores were extracted using aspect-based sentiment analysis, and two regression approaches, ordinary least squares (OLS) and spatial autoregressive combined models, were applied to evaluate how hotel specific features, such as the age and scale of the hotels and room rates, and their geographic characteristics, such as the proximity to airports and cultural landmarks, affect both emotional sentiment and formal hotel ratings. The OLS model for sentiment scores identified the scale and rating of the hotels as well as the proximity to the airports as key predictors. Additionally, the spatial autoregressive combined model was also statistically significant, suggesting spatial spillover effects. A separate model for the traditional rating revealed weaker associations, with only the hotel’s opening year reaching significance. These findings highlight a divergence between emotional responses and structured ratings, with sentiment scores more sensitive to spatial context. This study offers practical implications for hotel managers and urban planners, emphasizing the value of incorporating spatial factors into hospitality research to better understand the tourist experience. Full article

This paper introduces a general AI text review framework for the automated analysis of textual reviews using advanced natural language processing techniques. The framework uniquely integrates sentiment analysis, topic modeling, and abstractive summarization within a modular architecture. It leverages transformer-based models (e.g., DistilBERT and FASTopic), vector databases, and caching mechanisms to ensure scalability and real-time performance. To validate the general approach, we developed a domain-specific implementation, VisitorLens AI, which performs advanced textual analysis for Google Maps reviews of the UNESCO World Heritage Site, Kotor Fortress. We demonstrated that the designed system generates structured and actionable insights for both tourists and local authorities, and increases institutional capacity to evaluate UNESCO criteria compliance. Finally, we performed both quantitative and expert evaluations, demonstrating the high performance of our framework across NLP tasks. The outputs confirm the framework’s generalizability, robustness, and practical value across domains. Full article

Amid growing demands for heritage conservation and precision urban governance, this study proposes a multimodal framework to analyze tourist perception and behavior in Macau’s Historic Centre. We integrate geotagged social media images and text, ultra-wideband (UWB) pedestrian trajectories, and a LiDAR-derived 3D digital twin to examine the interplay among spatial configuration, movement, and affect. Visual content in tourist photos is classified with You Only Look Once (YOLOv8), and sentiment polarity in Weibo posts is estimated with a fine-tuned Bidirectional Encoder Representations from Transformers (BERT) model. UWB data provide fine-grained trajectories, and all modalities are georeferenced within the digital twin. Results indicate that iconic landmarks concentrate visual attention, pedestrian density, and positive sentiment, whereas peripheral sites show lower footfall yet strong emotional resonance. We further identify three coupling typologies that differentiate tourist experiences across spatial contexts. The study advances multimodal research on historic urban centers by delivering a reproducible framework that aligns image, text, and trajectory data to extract microscale patterns. Theoretically, it elucidates how spatial configuration, movement intensity, and affective expression co-produce experiential quality. Using Macau’s Historic Centre as an empirical testbed, the findings inform heritage revitalization, wayfinding, and crowd-management strategies. Full article

Social media platforms like Instagram significantly shape destination images and influence tourist behavior. Understanding how different cities are represented and perceived on these platforms is crucial for effective tourism marketing. This study provides a comparative analysis of Instagram content and engagement patterns in Seoul and Tokyo, two major Asian metropolises, to derive actionable marketing insights. We collected and analyzed 59,944 public Instagram posts geotagged or location-tagged within Seoul (n = 29,985) and Tokyo (n = 29,959). We employed a mixed-methods approach involving content categorization using a fine-tuned convolutional neural network (CNN) model, engagement metric analysis (likes, comments), Valence Aware Dictionary and sEntiment Reasoner (VADER) sentiment analysis and thematic classification of comments, geospatial analysis (Kernel Density Estimation [KDE], Moran’s I), and predictive modeling (Gradient Boosting with SHapley Additive exPlanations [SHAP] value analysis). A validation analysis using balanced samples (n = 2000 each) was conducted to address Tokyo’s lower geotagged data proportion. While both cities showed ‘Person’ as the dominant content category, notable differences emerged. Tokyo exhibited higher like-based engagement across categories, particularly for ‘Animal’ and ‘Food’ content, while Seoul generated slightly more comments, often expressing stronger sentiment. Qualitative comment analysis revealed Seoul comments focused more on emotional reactions, whereas Tokyo comments were often shorter, appreciative remarks. Geospatial analysis identified distinct hotspots. The validation analysis confirmed these spatial patterns despite Tokyo’s data limitations. Predictive modeling highlighted hashtag counts as the key engagement driver in Seoul and the presence of people in Tokyo. Seoul and Tokyo project distinct visual narratives and elicit different engagement patterns on Instagram. These findings offer practical implications for destination marketers, suggesting tailored content strategies and location-based campaigns targeting identified hotspots and specific content themes. This study underscores the value of integrating quantitative and qualitative analyses of social media data for nuanced destination marketing insights. Full article

In the context of cultural tourism integration, tourists’ spatial perception intention is an important carrier of spatial evaluation. In historic cultural districts represented by Jinan Mingfu City, tourists’ perceptual depth remains underexplored, leading to a misalignment between cultural tourism development and spatial quality needs. Taking Jinan Mingfu City as a representative case of a historic cultural district, while the living heritage model has revitalized local economies, the absence of a tourist perspective has resulted in misalignment between cultural tourism development and spatial quality requirements. This study establishes a technical framework encompassing “data crawling-factor aggregation-human-machine collaborative optimization”. It integrates Python web crawlers, SnowNLP sentiment analysis, and TF-IDF text mining technologies to extract physical elements; constructs a three-dimensional evaluation framework of “visual perception-spatial comfort-cultural experience” through SPSS principal component analysis; and quantifies physical element indicators such as green vision rate and signboard clutter index through street view semantic segmentation (OneFormer framework). A synergistic mechanism of machine scoring and manual double-blind scoring is adopted for correlation analysis to determine the impact degree of indicators and optimization strategies. This study identified that indicators such as green vision rate, shading facility coverage, and street enclosure ratio significantly influence tourist evaluations, with a severe deficiency in cultural spaces. Accordingly, it proposes targeted strategies, including visual landscape optimization, facility layout adjustment, and cultural scenario implementation. By breaking away from traditional qualitative evaluation paradigms, this study provides data-based support for the spatial quality enhancement of historic districts, thereby enabling the transformation of these areas from experience-oriented protection to data-driven intelligent renewal and promoting the sustainable development of cultural tourism. Full article

Current tourism route recommendation systems often overemphasize popular destinations, thereby overlooking geographical accessibility between attractions and the experiential coherence of the journey. Leveraging multidimensional attribute perceptions derived from tourist reviews, this study proposes a Spatial–Semantic Integrated Model for Tourist Attraction Representation (SSIM-TAR), which holistically encodes the composite attributes and multifaceted evaluations of attractions. Integrating these multidimensional features with inter-attraction relationships, three relational metrics are defined and fused: spatial proximity, resonance correlation, and thematic-sentiment similarity, forming a Tourist Attraction Multidimensional Association Network (MAN-SRT). This network enables precise characterization of complex inter-attraction dependencies. Building upon MAN-SRT, the Tourism Sentiment Chain (TSC) model is proposed that incorporates geographical accessibility, associative resonance, and thematic-sentiment synergy to optimize the selection and sequential arrangement of attractions in personalized route planning. Results demonstrate that SSIM-TAR effectively captures the integrated attributes and experiential quality of tourist attractions, while MAN-SRT reveals distinct multidimensional association patterns. Compared with popular platforms such as “Qunar” and “Mafengwo”, the TSC approach yields routes with enhanced spatial efficiency and thematic-sentiment coherence. This study advances tourism route modeling by jointly analyzing multidimensional experiential quality through spatial–semantic feature fusion and by achieving an integrated optimization of geographical accessibility and experiential coherence in route design. Full article

Ting, Tai, Lou, and Ge are types of ancient buildings that represent traditional Chinese architecture and culture. They are primarily constructed using mortise and tenon joints, complemented by brick and stone foundations, showcasing traditional architectural craftsmanship. However, research aimed at conserving, inheriting, and rejuvenating these buildings is limited, despite their status as Provincial Cultural Relic Protection Units of China. Therefore, the aim of this study was to reveal the spatial distribution of Ting, Tai, Lou, and Ge buildings across China, as well as the factors driving differences in their spatial distribution. Tourist experiences and building popularity were also explored. The spatial analysis method (e.g., Standard deviation ellipse and Geographic detector), Word cloud generation, and sentiment analysis, which uses Natural Language Processing techniques to identify subjective emotions in text, were applied to investigated the research issues. The key findings of this study are as follows. The ratio of Ting, Tai, Lou, and Ge buildings in Southeast China to that in Northwest China divided by the “Heihe–Tengchong” Line, an important demographic boundary in China with the ratio of permanent residents in the two areas remaining stable at 94:6, was 94.6:5.4. Geographic detector analysis revealed that six of the seven natural and socioeconomic factors (topography, waterways, roads, railways, population, and carbon dioxide emissions) had a significant influence on the spatial heterogeneity of these cultural heritage buildings in China, with socioeconomic factors, particularly population, having a greater influence on building spatial distributions. All seven factors (including the normalized difference vegetation index, an indicator used to assess vegetation health and coverage) were significant in Southeast China, whereas all factors were non-significant in Northwest China, which may be explained by the small number of buildings in the latter region. The average rating scores and heat scores for Ting, Tai, Lou, and Ge buildings were 4.35 (out of 5) and 3 (out of 10), respectively, reflecting an imbalance between service quality and popularity. According to the percentages of positive and negative reviews, Lou buildings have much better tourism services than other buildings, indicating a need to improve services to attract more tourists to Ting, Tai, and Ge buildings. Four main types of words were used with high frequency in the tourism reviews collected form Ctrip, a popular online travel platform in China: (1) historical stories; (2) tourism; (3) culture; and (4) cities/provinces. Ting and Tai buildings showed similar word clouds, as did Lou and Ge buildings, with only the former including historical stories. Conversely, landmark was a high-frequency word only in the reviews of Lou and Ge buildings. Specific suggestions were proposed based on the above findings to promote tourism and revive ancient Chinese architecture. Full article

Tourism plays an important role in the economic and social development of many countries and regions. Tourists buy food, such as canned food, alcohol, and spices, which increases the value of a trip, fulfilling a cultural, sentimental, educational, and marketing role whilst documenting the trip, or they become gifts for family and friends. However, spices may not be of the appropriate quality or may even be harmful to health due to contamination or adulteration. Therefore, the aim of the paper was to present spices as culinary souvenirs and to indicate some risks that may arise from their consumption. To date, only few such studies have been published in this area. A literature review was conducted and data from Eurostat, Rapid Alert System for Food and Feed (RASFF) and Web of Science were used. The most serious hazards in spices are pathogens, pesticides, and mycotoxins in products from Asia. Adequate awareness needs to be built among tourists and tour operators about where to buy spices that are risk-free and not adulterated. It will contribute to the development of sustainable food tourism. Further research may look at specific types of spices and where they are purchased highlighting the issue of authenticity and traceability. Full article

Urban green spaces (UGSs) provide recreational and cultural services to urban residents and play an important role in mental health. This study uses big data mining techniques to analyze 62 urban parks in the Changsha–Zhuzhou–Xiangtan urban agglomeration (CZXUA) based on data such as points of interest (POIs), areas of interest (AOIs), and user comments from the popular social media platform Dianping. In addition, the authors apply sentiment analysis using perceptual dictionaries combined with geographic information data to identify text emotions. A structural equation model (SEM) was constructed in IBM SPSS AMOS 24.0 software to investigate the relationship between five external features, five types of cultural services, nine landscape elements, four environmental factors, and tourist emotions. The results show that UGS external features, cultural services, landscape elements, and environmental factors all have positive effects on residents’ emotions, with landscape elements having the greatest impact. The other factors show similar effects on residents’ moods. In various UGSs, natural elements such as vegetation and water tend to evoke positive emotions in residents, while artificial elements such as roads, squares, and buildings elicit more varied emotional responses. This research provides science-based support for the design and management of urban parks. Full article

With the rapid development of digital networks and communication technologies, traditional word-of-mouth (WOM) has transformed into electronic word-of-mouth (eWOM), which plays a pivotal role in improving the management and service quality of ancient town tourism. This study uses Python web scraping techniques to gather eWOM data from the top ten ancient towns in southern China. Using IPA analysis, the analytic hierarchy process (AHP), Term Frequency–Inverse Document Frequency (TF-IDF), and cluster analysis, we developed a comprehensive eWOM evaluation framework. This framework was employed to perform word frequency analysis, sentiment analysis, topic modeling, and rating analysis, providing deeper insights into tourists’ perceptions. The results reveal several key findings: (1) Transportation infrastructure varies significantly across the towns. Heshun and Huangyao suffer from poor accessibility, while the remaining towns benefit from the developed transportation network of the Yangtze River Delta. (2) The volume of eWOM is strongly influenced by seasonal patterns and was notably impacted by the COVID-19 pandemic. (3) The majority of tourists express positive sentiments toward the ancient towns, with a focus on the available facilities. Their highest levels of satisfaction, however, are associated with the scenic landscapes. (4) A comprehensive eWOM analysis suggests that Wuzhen and Xidi–Hongcun are the most popular tourist destinations, while Zhujiajiao, Huangyao, Zhouzhuang, and Nanxun exhibit lower levels of both attention and visitor satisfaction. Full article

Tourism is a critical sector for economic growth and cultural exchange, particularly for destinations like Turkey, which consistently attracts millions of visitors annually. This study investigates the dynamics of tourism demand in Turkey between 2008 and 2024, with a focus on seasonality, long-term trends, and predictive modeling accuracy. Time-series data were analyzed, and the impacts of economic indicators and digital search trends were evaluated using SARIMA and SARIMAX models. The results demonstrate that the SARIMA models outperformed the SARIMAX models, highlighting the dominance of intrinsic seasonal patterns over external regressors, such as exchange rates and inflation. The findings emphasize that geographic proximity and cultural similarities drive consistent tourist flows, while behavioral data like Google Trends provide supplementary insights into demand shifts. However, economic variables showed limited short-term predictive power. These results underscore the importance of prioritizing time-series structures in forecasting frameworks while complementing them with behavioral indicators for enhanced accuracy. This study contributes to the literature by addressing a critical gap in understanding how various factors influence tourism demand in Turkey and offers practical implications for policymakers and tourism planners to optimize strategic planning and resource allocation, ensuring sustainable tourism growth. Future research should explore hybrid models that incorporate sentiment-driven data and cultural factors for more robust forecasting. Full article

Online reviews are crucial for identifying the factors that affect the dynamic evolution of tourist sentiment, improving tourist satisfaction. This study employs pre-trained models BERT and BERTopic and social network analysis to examine 228,062 reviews collected from Ctrip using Python. The factors influencing tourist sentiment across natural tourism attractions (NTAs), cultural tourism attractions (CTAs), and theme park tourism attractions (TPTAs) were explored before, during, and after the pandemic. The findings reveal that there was minimal change in the types of factors influencing before and during the pandemic, significant changes in the values of factors during the pandemic, and fluctuations in both the types and values of factors after the pandemic. Regardless of the period, influences on negative sentiment were more dispersed, while positive emotions were more polarized. Based on these insights, we propose theoretical contributions and improvement strategies for enhancing resilience and promoting high-quality development in different types of attractions. Full article

The growth of the tourism industry in Southeast Asia, particularly in Indonesia, Thailand, and Vietnam, establishes the region as a leading global tourism destination. Numerous studies have explored tourist behavior within specific regions. However, the question of whether tourists’ experience perceptions differ based on their cultural backgrounds is still insufficiently addressed. Previous articles suggest that an individual’s cultural background plays a significant role in shaping tourist values and expectations. This study investigates how tourists’ cultural backgrounds, represented by their geographical regions of origin, impact their entertainment experiences, sentiments, and mobility patterns across the three countries. We gathered 387,010 TripAdvisor reviews and analyzed them using a combination of advanced text mining techniques and network analysis to map tourist mobility patterns. Comparing sentiments and behaviors across cultural backgrounds, this study found that entertainment preferences vary by origin. The network analysis reveals distinct exploration patterns: diverse and targeted exploration. Vietnam achieves the highest satisfaction across the cultural groups through balanced development, while Thailand’s integrated entertainment creates cultural divides, and Indonesia’s generates moderate satisfaction regardless of cultural background. This study contributes to understanding tourism dynamics in Southeast Asia through a data-driven, comparative analysis of tourist behaviors. The findings provide insights for destination management, marketing strategies, and policy development, highlighting the importance of tailoring tourism offerings to meet the diverse preferences of visitors from different global regions. Full article

In alignment with Saudi Vision 2030’s strategic objectives to diversify and enhance the tourism sector, this study explores the integration of Artificial Intelligence (AI) in the Al-Baha district, a prime tourist destination in Saudi Arabia. Our research introduces a hybrid AI-based framework that leverages sentiment analysis to assess and enhance tourist satisfaction, capitalizing on data extracted from social media platforms such as YouTube. This framework seeks to improve the quality of tourism experiences and augment the business value within the region. By analyzing sentiments expressed in user-generated content, the proposed AI system provides real-time insights into tourist preferences and experiences, enabling targeted interventions and improvements. The conducted experiments demonstrated the framework’s efficacy in identifying positive, neutral and negative sentiments, with the Multinomial Naive Bayes classifier showing superior performance in terms of precision and recall. These results indicate significant potential for AI to transform tourism practices in Al-Baha, offering enhanced experiences to visitors and driving the economic sustainability of the sector in line with the national vision. This study underscores the transformative potential of AI in refining operational strategies and aligning them with evolving tourist expectations, thereby supporting the broader goals of Saudi Vision 2030 for the tourism industry. Full article