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Analyzing user reviews in tourism with topic models

Author

Listed:
  • Marco Rossetti

    (University of Milano-Bicocca)

  • Fabio Stella

    (University of Milano-Bicocca)

  • Markus Zanker

    (Alpen-Adria-Universität Klagenfurt)

Abstract

User generated content in general and textual reviews in particular constitute a vast source of information for the decision making of tourists and management and are therefore a key component for e-tourism. This paper provides a description of the topic model method with a particular application focus on the tourism domain. It therefore contributes different application scenarios where the topic model method processes textual reviews in order to provide decision support and recommendations to online tourists as well as to build a basis for further analytics. In the latter case the delivery of additional semantics helps digging into the enormous amounts of data that are continuously collected in present time. The contribution therefore consists of new models based on the topic model method and results from experimenting with user generated review data on restaurants and hotels.

Suggested Citation

  • Marco Rossetti & Fabio Stella & Markus Zanker, 2016. "Analyzing user reviews in tourism with topic models," Information Technology & Tourism, Springer, vol. 16(1), pages 5-21, March.
    • Handle: RePEc:spr:infott:v:16:y:2016:i:1:d:10.1007_s40558-015-0035-y
    DOI: 10.1007/s40558-015-0035-y
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    References listed on IDEAS

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    Cited by:

    1. Montserrat Jiménez-Partearroyo & Ana Medina-López & Sudhir Rana, 2024. "Business intelligence and business analytics in tourism: insights through Gioia methodology," International Entrepreneurship and Management Journal, Springer, vol. 20(3), pages 2287-2321, September.
    2. Antonio Moreno-Ortiz & Soluna Salles-Bernal & Aroa Orrequia-Barea, 2019. "Design and validation of annotation schemas for aspect-based sentiment analysis in the tourism sector," Information Technology & Tourism, Springer, vol. 21(4), pages 535-557, December.
    3. Choi, Hyunhong & Woo, JongRoul, 2022. "Investigating emerging hydrogen technology topics and comparing national level technological focus: Patent analysis using a structural topic model," Applied Energy, Elsevier, vol. 313(C).
    4. Julia Neidhardt & Nataliia Rümmele & Hannes Werthner, 0. "Predicting happiness: user interactions and sentiment analysis in an online travel forum," Information Technology & Tourism, Springer, vol. 0, pages 1-19.
    5. Julia Neidhardt & Nataliia Rümmele & Hannes Werthner, 2017. "Predicting happiness: user interactions and sentiment analysis in an online travel forum," Information Technology & Tourism, Springer, vol. 17(1), pages 101-119, March.
    6. Pantano, Eleonora & Priporas, Constantinos-Vasilios & Stylos, Nikolaos, 2017. "‘You will like it!’ using open data to predict tourists' response to a tourist attraction," Tourism Management, Elsevier, vol. 60(C), pages 430-438.
    7. Georgia Yfantidou & Sofia Yfantidou & Dimitra Karanatsiou & Angelos Liontakis & Mairi Maniopoulou & Panagiota Balaska & Alkistis Papaioannou & Vassiliki Vassilopoulou, 2024. "Experience Economy Perspective on Recreational Fishing Tourism Travelers’ Reviews: A Data Science Approach," Tourism and Hospitality, MDPI, vol. 5(2), pages 1-27, April.
    8. 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.
    9. Michela Fazzolari & Marinella Petrocchi, 2018. "A study on online travel reviews through intelligent data analysis," Information Technology & Tourism, Springer, vol. 20(1), pages 37-58, December.

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