IDEAS home Printed from https://ideas.repec.org/a/gam/jftint/v17y2025i7p276-d1685304.html

Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews

Author

Listed:
  • Bosen Li

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Rui Li

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China
    Collaborative Innovation Center of Geospatial Technology, Wuhan University, Wuhan 430079, China)

  • Junhao Wang

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

  • Aihong Song

    (State Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430079, China)

Abstract

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.

Suggested Citation

  • Bosen Li & Rui Li & Junhao Wang & Aihong Song, 2025. "Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews," Future Internet, MDPI, vol. 17(7), pages 1-30, June.
    • Handle: RePEc:gam:jftint:v:17:y:2025:i:7:p:276-:d:1685304
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1999-5903/17/7/276/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1999-5903/17/7/276/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gang Ren & Taeho Hong, 2017. "Investigating Online Destination Images Using a Topic-Based Sentiment Analysis Approach," Sustainability, MDPI, vol. 9(10), pages 1-18, September.
    2. Maozheng Fu & Ling Pan & Zaoli Yang, 2022. "Sentiment Analysis of Tourist Scenic Spots Internet Comments Based on LSTM," Mathematical Problems in Engineering, Hindawi, vol. 2022, pages 1-9, July.
    3. Juanjuan Chen & Liying Huang & Chengliang Wang & Nijia Zheng, 2020. "Discovering Travel Spatiotemporal Pattern Based on Sequential Events Similarity," Complexity, Hindawi, vol. 2020, pages 1-10, December.
    4. Pei Lin & Lili Chen & Zeshun Luo, 2022. "Analysis of Tourism Experience in Haizhu National Wetland Park Based on Web Text," Sustainability, MDPI, vol. 14(5), pages 1-16, March.
    5. Alireza Alaei & Ying Wang & Vinh Bui & Bela Stantic, 2023. "Target-Oriented Data Annotation for Emotion and Sentiment Analysis in Tourism Related Social Media Data," Future Internet, MDPI, vol. 15(4), pages 1-21, April.
    6. Shu-Fen Tu & Ching-Sheng Hsu & Yu-Tzu Lu, 2021. "Improving RE-SWOT Analysis with Sentiment Classification: A Case Study of Travel Agencies," Future Internet, MDPI, vol. 13(9), pages 1-17, August.
    7. Qin Li & Shaobo Li & Jie Hu & Sen Zhang & Jianjun Hu, 2018. "Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors," Sustainability, MDPI, vol. 10(9), pages 1-15, September.
    8. Chryssy Potsiou & Charalabos Ioannidis & Sofia Soile & Argyro-Maria Boutsi & Regina Chliverou & Konstantinos Apostolopoulos & Maria Gkeli & Fotis Bourexis, 2023. "Geospatial Tool Development for the Management of Historical Hiking Trails—The Case of the Holy Site of Meteora," Land, MDPI, vol. 12(8), pages 1-19, August.
    9. Saizo Aoyagi & Yiping Le & Tetsuo Shimizu & Kazuki Takahashi, 2020. "Mobile Application to Provide Traffic Congestion Estimates and Tourism Spots to Promote Additional Stopovers," Future Internet, MDPI, vol. 12(5), pages 1-11, April.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yanlong Guo & Jiaying Yu & Han Zhang & Zuoqing Jiang, 2022. "A Study on Cultural Context Perception in Huizhou Cultural and Ecological Reserve Based on Multi-Criteria Decision Analysis," Sustainability, MDPI, vol. 14(24), pages 1-20, December.
    2. Monica Moscatelli, 2024. "Heritage as a Driver of Sustainable Tourism Development: The Case Study of the Darb Zubaydah Hajj Pilgrimage Route," Sustainability, MDPI, vol. 16(16), pages 1-24, August.
    3. Vladimir Stojanović & Maja Mijatov Ladičorbić & Aleksandra S. Dragin & Marija Cimbaljević & Sanja Obradović & Dragan Dolinaj & Tamara Jovanović & Anđelija Ivkov-Džigurski & Jelena Dunjić & Milena Nede, 2023. "Tourists’ Motivation in Wetland Destinations: Gornje Podunavlje Special Nature Reserve Case Study (Mura-Drava-Danube Transboundary Biosphere Reserve)," Sustainability, MDPI, vol. 15(12), pages 1-21, June.
    4. Berny Carrera & Jae-Yoon Jung, 2018. "SentiFlow: An Information Diffusion Process Discovery Based on Topic and Sentiment from Online Social Networks," Sustainability, MDPI, vol. 10(8), pages 1-16, August.
    5. Ziye Shang & Jian Ming Luo, 2022. "Topic Modeling for Hiking Trail Online Reviews: Analysis of the Mutianyu Great Wall," Sustainability, MDPI, vol. 14(6), pages 1-16, March.
    6. Ana Luque & Luiz Alves, 2024. "The Importance of Automatic Counters for Sustainable Management in Rural Areas: The Case of Hiking Trails in Historic Villages of Portugal," Land, MDPI, vol. 14(1), pages 1-19, December.
    7. Sergei Mikhailov & Alexey Kashevnik, 2020. "Tourist Behaviour Analysis Based on Digital Pattern of Life—An Approach and Case Study," Future Internet, MDPI, vol. 12(10), pages 1-16, September.
    8. Tomás Bernal-Beltrán & Mario Andrés Paredes-Valverde & María del Pilar Salas-Zárate & José Antonio García-Díaz & Rafael Valencia-García, 2025. "Sentiment Analysis in Mexican Spanish: A Comparison Between Fine-Tuning and In-Context Learning with Large Language Models," Future Internet, MDPI, vol. 17(10), pages 1-23, September.
    9. Yuguo Tao & Feng Zhang & Chunyun Shi & Yun Chen, 2019. "Social Media Data-Based Sentiment Analysis of Tourists’ Air Quality Perceptions," Sustainability, MDPI, vol. 11(18), pages 1-23, September.
    10. Jessica Bollenbach & Dominik Rebholz & Robert Keller, 2025. "The road not taken: Representing expert knowledge for route similarities in sustainable tourism using machine learning," Electronic Markets, Springer;IIM University of St. Gallen, vol. 35(1), pages 1-21, December.
    11. Siya Cheng & Zheran Zhai & Wenzhuo Sun & Yuan Wang & Rui Yu & Xiaoyu Ge, 2022. "Research on the Satisfaction of Beijing Waterfront Green Space Landscape Based on Social Media Data," Land, MDPI, vol. 11(10), pages 1-25, October.
    12. Ulugbek Hudayberdiev & Junyeong Lee & Odil Fayzullaev, 2025. "Smart Tourism Landmark Recognition: A Multi-Threshold Enhancement and Selective Ensemble Approach Using YOLO11," Sustainability, MDPI, vol. 17(17), pages 1-26, September.
    13. Zhongzhong Zeng & Meizhu Wang & Dingyi Liu & Xuan Yu & Bo Zhang, 2024. "A Semantic Analysis Method of Public Public Built Environment and Its Landscape Based on Big Data Technology: Kimbell Art Museum as Example," Land, MDPI, vol. 13(5), pages 1-16, May.
    14. Xiaopiao Yang & Yuluan Zhao & Jia Zhao & Chao Shi & Bailu Deng, 2022. "Tourists’ Perceived Attitudes toward the Famous Terraced Agricultural Cultural Heritage Landscape in China," Agriculture, MDPI, vol. 12(9), pages 1-13, September.
    15. Virginica Rusu & Cristian Rusu & Nicolás Matus & Federico Botella, 2023. "Tourist Experience Challenges: A Holistic Approach," Sustainability, MDPI, vol. 15(17), pages 1-32, August.
    16. Sawitree Srianan & Aziz Nanthaamornphong & Chayanon Phucharoen, 2025. "Advancing tourism sentiment analysis: a comparative evaluation of traditional machine learning, deep learning, and transformer models on imbalanced datasets," Information Technology & Tourism, Springer, vol. 27(4), pages 1011-1045, December.
    17. Guan, Keqin & Gong, Xu, 2023. "A new hybrid deep learning model for monthly oil prices forecasting," Energy Economics, Elsevier, vol. 128(C).
    18. Dongfang Ma & Shaojie Zhang & Tieqiao Xiao & Taotao Shui, 2023. "Aspects of New and Old Urban Parks Most Valued by Residents on Social Media: A Case Study in Hefei," Sustainability, MDPI, vol. 15(18), pages 1-21, September.
    19. Pegado-Bardayo, Ana & Lorenzo-Espejo, Antonio & Muñuzuri, Jesús & Aparicio-Ruiz, Pablo, 2023. "A data-driven decision support system for service completion prediction in last mile logistics," Transportation Research Part A: Policy and Practice, Elsevier, vol. 176(C).
    20. Qin Li & Shaobo Li & Jie Hu & Sen Zhang & Jianjun Hu, 2018. "Tourism Review Sentiment Classification Using a Bidirectional Recurrent Neural Network with an Attention Mechanism and Topic-Enriched Word Vectors," Sustainability, MDPI, vol. 10(9), pages 1-15, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jftint:v:17:y:2025:i:7:p:276-:d:1685304. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.