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Mining big data in tourism

Author

Listed:
  • Carmela Iorio

    (University of Naples Federico II)

  • Giuseppe Pandolfo

    (University of Naples Federico II)

  • Antonio D’Ambrosio

    (University of Naples Federico II)

  • Roberta Siciliano

    (University of Naples Federico II)

Abstract

Knowledge discovery from various sources of information based on different data types for decision and accurate prediction can be rather complex and costly without a statistical information system. In Big Data Era, Statistical Tourism Observatory needs to be revised. This paper introduces a conceptual model of Digital Tourism System (DTS) where various types of standard and non-standard data can be processed by actors and spectators in tourism sector. Particularly, big data can be very useful and the figure of Data Scientist within the tourism industry becomes prominent. DTS allows to emphasize four knowledge areas of interest for different purposes, specifically, destination management, research and innovation, market analysis, labor market, in order to improve tourism management and research. Key steps of the knowledge discovery pyramid are exploited to provide an added value in decision-making on the basis of statistical learning methods. Two examples are shown, mining online textual and photo data respectively.

Suggested Citation

  • Carmela Iorio & Giuseppe Pandolfo & Antonio D’Ambrosio & Roberta Siciliano, 2020. "Mining big data in tourism," Quality & Quantity: International Journal of Methodology, Springer, vol. 54(5), pages 1655-1669, December.
    • Handle: RePEc:spr:qualqt:v:54:y:2020:i:5:d:10.1007_s11135-019-00927-0
    DOI: 10.1007/s11135-019-00927-0
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    1. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
    2. Li, Xin & Pan, Bing & Law, Rob & Huang, Xiankai, 2017. "Forecasting tourism demand with composite search index," Tourism Management, Elsevier, vol. 59(C), pages 57-66.
    3. Roberta Siciliano & Antonio D’Ambrosio & Massimo Aria & Sonia Amodio, 2016. "Erratum to: Analysis of Web Visit Histories, Part I: Distance-Based Visualization of Sequence Rules," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 325-325, July.
    4. Zhang, Lingyun & Lan, Chaoying & Qi, Fei & Wu, Ping, 2017. "Development pattern, classification and evaluation of the tourism academic community in China in the last ten years: From the perspective of big data of articles of tourism academic journals," Tourism Management, Elsevier, vol. 58(C), pages 235-244.
    5. Xiang, Zheng & Du, Qianzhou & Ma, Yufeng & Fan, Weiguo, 2017. "A comparative analysis of major online review platforms: Implications for social media analytics in hospitality and tourism," Tourism Management, Elsevier, vol. 58(C), pages 51-65.
    6. Rosario D’Agata & Simona Gozzo & Venera Tomaselli, 2013. "Network analysis approach to map tourism mobility," Quality & Quantity: International Journal of Methodology, Springer, vol. 47(6), pages 3167-3184, October.
    7. Fang, Bin & Ye, Qiang & Kucukusta, Deniz & Law, Rob, 2016. "Analysis of the perceived value of online tourism reviews: Influence of readability and reviewer characteristics," Tourism Management, Elsevier, vol. 52(C), pages 498-506.
    8. Zhang, Ziqiong & Zhang, Zili & Yang, Yang, 2016. "The power of expert identity: How website-recognized expert reviews influence travelers' online rating behavior," Tourism Management, Elsevier, vol. 55(C), pages 15-24.
    9. Roberta Siciliano & Antonia D’Ambrosio & Massimo Aria & Sonia Amodio, 2016. "Analysis of Web Visit Histories, Part I: Distance-Based Visualization of Sequence Rules," Journal of Classification, Springer;The Classification Society, vol. 33(2), pages 298-324, July.
    10. Yang, Xin & Pan, Bing & Evans, James A. & Lv, Benfu, 2015. "Forecasting Chinese tourist volume with search engine data," Tourism Management, Elsevier, vol. 46(C), pages 386-397.
    11. Guo, Yue & Barnes, Stuart J. & Jia, Qiong, 2017. "Mining meaning from online ratings and reviews: Tourist satisfaction analysis using latent dirichlet allocation," Tourism Management, Elsevier, vol. 59(C), pages 467-483.
    12. Költringer, Clemens & Dickinger, Astrid, 2015. "Analyzing destination branding and image from online sources: A web content mining approach," Journal of Business Research, Elsevier, vol. 68(9), pages 1836-1843.
    13. Roberta Siciliano & Antonio D’Ambrosio & Massimo Aria & Sonia Amodio, 2017. "Analysis of Web Visit Histories, Part II: Predicting Navigation by Nested STUMP Regression Trees," Journal of Classification, Springer;The Classification Society, vol. 34(3), pages 473-493, October.
    14. Vu, Huy Quan & Li, Gang & Law, Rob & Ye, Ben Haobin, 2015. "Exploring the travel behaviors of inbound tourists to Hong Kong using geotagged photos," Tourism Management, Elsevier, vol. 46(C), pages 222-232.
    15. Amodio, S. & D’Ambrosio, A. & Siciliano, R., 2016. "Accurate algorithms for identifying the median ranking when dealing with weak and partial rankings under the Kemeny axiomatic approach," European Journal of Operational Research, Elsevier, vol. 249(2), pages 667-676.
    16. Antonio D’Ambrosio & Carmela Iorio & Michele Staiano & Roberta Siciliano, 2019. "Median constrained bucket order rank aggregation," Computational Statistics, Springer, vol. 34(2), pages 787-802, June.
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    Cited by:

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    2. Federico Ninivaggi & Eleonora Cutrini, 2024. "Exploring local well-being and vulnerability through OpenStreetMap: the case of Italy," Quality & Quantity: International Journal of Methodology, Springer, vol. 58(4), pages 3435-3473, August.
    3. Gianluca Solazzo & Ylenia Maruccia & Valentina Ndou & Pasquale Del Vecchio, 2022. "How to exploit Big Social Data in the Covid-19 pandemic: the case of the Italian tourism industry," Service Business, Springer;Pan-Pacific Business Association, vol. 16(3), pages 417-443, September.

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