IDEAS home Printed from https://ideas.repec.org/a/eee/tefoso/v123y2017icp362-369.html
   My bibliography  Save this article

What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management

Author

Listed:
  • Kim, Kun
  • Park, Oun-joung
  • Yun, Seunghyun
  • Yun, Haejung

Abstract

Recently, the Internet has brought a big change in tourists' behavior patterns. Travelers not only reserve hotels and airline tickets online, but also exchange travel information and descriptions of pleasant or unpleasant travel experiences through online review sites and personal travel blogs. In spite of the increasing use of online channels, application of online text data has been limited since the volume of the data set is too large to analyze manually and comprehensively. With recent technological advances in processing big data online, consumer-generated information can be automatically analyzed by artificial intelligence.

Suggested Citation

  • Kim, Kun & Park, Oun-joung & Yun, Seunghyun & Yun, Haejung, 2017. "What makes tourists feel negatively about tourism destinations? Application of hybrid text mining methodology to smart destination management," Technological Forecasting and Social Change, Elsevier, vol. 123(C), pages 362-369.
    • Handle: RePEc:eee:tefoso:v:123:y:2017:i:c:p:362-369
    DOI: 10.1016/j.techfore.2017.01.001
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0040162517300112
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.techfore.2017.01.001?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Carroll, Christopher D & Fuhrer, Jeffrey C & Wilcox, David W, 1994. "Does Consumer Sentiment Forecast Household Spending? If So, Why?," American Economic Review, American Economic Association, vol. 84(5), pages 1397-1408, December.
    2. Zongshui Wang & Hong Zhao & Yan Wang, 2015. "Social networks in marketing research 2001–2014: a co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(1), pages 65-82, October.
    3. Dimitrios Buhalis & Aditya Amaranggana, 2015. "Smart Tourism Destinations Enhancing Tourism Experience Through Personalisation of Services," Springer Books, in: Iis Tussyadiah & Alessandro Inversini (ed.), Information and Communication Technologies in Tourism 2015, edition 127, pages 377-389, Springer.
    4. Gary Akehurst, 2009. "User generated content: the use of blogs for tourism organisations and tourism consumers," Service Business, Springer;Pan-Pacific Business Association, vol. 3(1), pages 51-61, March.
    5. Shawn Mankad & Hyunjeong Spring Han & Joel Goh & Srinagesh Gavirneni, 2016. "Understanding Online Hotel Reviews Through Automated Text Analysis," Post-Print hal-02311939, HAL.
    6. Henry Small, 1973. "Co‐citation in the scientific literature: A new measure of the relationship between two documents," Journal of the American Society for Information Science, Association for Information Science & Technology, vol. 24(4), pages 265-269, July.
    7. Frederic S. Mishkin, 1978. "Consumer Sentiment and Spending on Consumer Durables," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 9(1), pages 217-232.
    8. Bei-Ni Yan & Tian-Shyug Lee & Tsung-Pei Lee, 2015. "Mapping the intellectual structure of the Internet of Things (IoT) field (2000–2014): a co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(2), pages 1285-1300, November.
    9. Adrian W. Throop, 1992. "Consumer sentiment: its causes and effects," Economic Review, Federal Reserve Bank of San Francisco, pages 35-59.
    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. Hatice Gökçe Karasoy Can & Çağlar Yüncüler, 2018. "The Explanatory Power and the Forecast Performance of Consumer Confidence Indices for Private Consumption Growth in Turkey," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 54(9), pages 2136-2152, July.
    2. Carlos Olmeda-Gómez & Maria-Antonia Ovalle-Perandones & Antonio Perianes-Rodríguez, 2017. "Co-word analysis and thematic landscapes in Spanish information science literature, 1985–2014," Scientometrics, Springer;Akadémiai Kiadó, vol. 113(1), pages 195-217, October.
    3. Sercin Sahin, 2021. "Consumer confidence, consumption, and macroeconomic fluctuations: A systemic stock‐flow consistent model," Metroeconomica, Wiley Blackwell, vol. 72(4), pages 868-904, November.
    4. Manosso, Franciele Cristina & Domareski Ruiz, Thays Cristina, 2021. "Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 7, pages 16-27.
    5. Cristina Franciele & Thays Christina Domareski Ruiz, 2021. "Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review," Post-Print hal-03373984, HAL.
    6. Gric, Zuzana & Ehrenbergerova, Dominika & Hodula, Martin, 2022. "The power of sentiment: Irrational beliefs of households and consumer loan dynamics," Journal of Financial Stability, Elsevier, vol. 59(C).
    7. Kajal Lahiri & Yongchen Zhao, 2016. "Determinants of Consumer Sentiment Over Business Cycles: Evidence from the US Surveys of Consumers," Journal of Business Cycle Research, Springer;Centre for International Research on Economic Tendency Surveys (CIRET), vol. 12(2), pages 187-215, December.
    8. Sarah Brown & Mark N. Harris & Christopher Spencer & Karl Taylor, 2024. "Financial Expectations and Household Consumption: Does Middle‐Inflation Matter?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 56(4), pages 741-768, June.
    9. Aliakbar Pourhatami & Mohammad Kaviyani-Charati & Bahareh Kargar & Hamed Baziyad & Maryam Kargar & Carlos Olmeda-Gómez, 2021. "Mapping the intellectual structure of the coronavirus field (2000–2020): a co-word analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(8), pages 6625-6657, August.
    10. César Carrera & Miguel Puch, 2019. "Consumption dynamics and the expectation channel in a Small Open Economy," Working Papers 144, Peruvian Economic Association.
    11. Vuchelen, Jef, 2004. "Consumer sentiment and macroeconomic forecasts," Journal of Economic Psychology, Elsevier, vol. 25(4), pages 493-506, August.
    12. Faraji, Omid & Ezadpour, Mostafa & Rahrovi Dastjerdi, Alireza & Dolatzarei, Ehsan, 2022. "Conceptual structure of balanced scorecard research: A co-word analysis," Evaluation and Program Planning, Elsevier, vol. 94(C).
    13. Brigitte Desroches & Marc-André Gosselin, 2002. "The Usefulness of Consumer Confidence Indexes in the United States," Staff Working Papers 02-22, Bank of Canada.
    14. Takano, Yasutomo & Kajikawa, Yuya, 2019. "Extracting commercialization opportunities of the Internet of Things: Measuring text similarity between papers and patents," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 45-68.
    15. Pami Dua, 2008. "Analysis of Consumers’ Perceptions of Buying Conditions for Houses," The Journal of Real Estate Finance and Economics, Springer, vol. 37(4), pages 335-350, November.
    16. Choy, Keen Meng & Leong, Kenneth & Tay, Anthony S., 2006. "Non-fundamental expectations and economic fluctuations: Evidence from professional forecasts," Journal of Macroeconomics, Elsevier, vol. 28(2), pages 446-460, June.
    17. Salhin, Ahmed & Sherif, Mohamed & Jones, Edward, 2016. "Managerial sentiment, consumer confidence and sector returns," International Review of Financial Analysis, Elsevier, vol. 47(C), pages 24-38.
    18. Kim, Jikyung (Jeanne) & Dong, Hang & Choi, Jeonghye & Chang, Sue Ryung, 2022. "Sentiment change and negative herding: Evidence from microblogging and news," Journal of Business Research, Elsevier, vol. 142(C), pages 364-376.
    19. Del Missier, Fabio & Ranyard, Rob & Bonini, Nicolao, 2016. "Perceived inflation: The role of product accessibility and attitudes towards inflation," Journal of Economic Psychology, Elsevier, vol. 56(C), pages 97-106.
    20. Andy C. C. Kwan & John A. Cotsomitis, 2004. "Can Consumer Attitudes Forecast Household Spending in the United States? Further Evidence from the Michigan Survey of Consumers," Southern Economic Journal, John Wiley & Sons, vol. 71(1), pages 136-144, July.

    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:eee:tefoso:v:123:y:2017:i:c:p:362-369. 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: Catherine Liu (email available below). General contact details of provider: http://www.sciencedirect.com/science/journal/00401625 .

    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.