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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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    1. Xiang, Zheng & Gretzel, Ulrike, 2010. "Role of social media in online travel information search," Tourism Management, Elsevier, vol. 31(2), pages 179-188.
    2. Zehrer, Anita & Crotts, John C. & Magnini, Vincent P., 2011. "The perceived usefulness of blog postings: An extension of the expectancy-disconfirmation paradigm," Tourism Management, Elsevier, vol. 32(1), pages 106-113.
    3. Scott Deerwester & Susan T. Dumais & George W. Furnas & Thomas K. Landauer & Richard Harshman, 1990. "Indexing by latent semantic analysis," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 41(6), pages 391-407, September.
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    Cited by:

    1. 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.
    2. 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.
    3. 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.
    4. 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.
    5. 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).
    6. 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.
    7. 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.

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