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Automatic analysis of textual hotel reviews

Author

Listed:
  • Aitor García-Pablos

    (Vicomtech-IK4)

  • Montse Cuadros

    (Vicomtech-IK4)

  • Maria Teresa Linaza

    (Vicomtech-IK4)

Abstract

Social Media and consumer-generated content continue to grow and impact the hospitality domain. Consumers write online reviews to indicate their level of satisfaction with a hotel and inform other consumers on the Internet of their hotel stay experience. A number of websites specialized in tourism and hospitality have flourished on the Web (e.g. Tripadvisor). The tremendous growth of these data-generating sources demands new tools to deal with them. To cope with big amounts of customer-generated reviews and comments, Natural Language Processing (NLP) tools have become necessary to automatically process and manage textual customer reviews (e.g. to perform Sentiment Analysis). This work describes OpeNER, a NLP platform applied to the hospitality domain to automatically process customer-generated textual content and obtain valuable information from it. The presented platform consists of a set of Open Source and free NLP tools to analyse text based on a modular architecture to ease its modification and extension. The training and evaluation has been performed using a set of manually annotated hotel reviews gathered from websites like Zoover and HolidayCheck.

Suggested Citation

  • Aitor García-Pablos & Montse Cuadros & Maria Teresa Linaza, 2016. "Automatic analysis of textual hotel reviews," Information Technology & Tourism, Springer, vol. 16(1), pages 45-69, March.
    • Handle: RePEc:spr:infott:v:16:y:2016:i:1:d:10.1007_s40558-015-0047-7
    DOI: 10.1007/s40558-015-0047-7
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    References listed on IDEAS

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    1. Liu, Zhiwei & Park, Sangwon, 2015. "What makes a useful online review? Implication for travel product websites," Tourism Management, Elsevier, vol. 47(C), pages 140-151.
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    Cited by:

    1. 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.
    2. Enrique Bigne & Carla Ruiz & Carmen Perez-Cabañero & Antonio Cuenca, 2023. "Are customer star ratings and sentiments aligned? A deep learning study of the customer service experience in tourism destinations," Service Business, Springer;Pan-Pacific Business Association, vol. 17(1), pages 281-314, March.
    3. Arpan Kumar Kar & Shweta Kumari Choudhary & P. Vigneswara Ilavarasan, 2023. "How can we improve tourism service experiences: insights from multi-stakeholders’ interaction," DECISION: Official Journal of the Indian Institute of Management Calcutta, Springer;Indian Institute of Management Calcutta, vol. 50(1), pages 73-89, March.
    4. Yuan-Yuan Wang & Yuan-Ying Chi & Jin-Hua Xu & Jia-Lin Li, 2021. "Consumer Preferences for Electric Vehicle Charging Infrastructure Based on the Text Mining Method," Energies, MDPI, vol. 14(15), pages 1-20, July.
    5. Erum Haris & Keng Hoon Gan, 2017. "Mining graphs from travel blogs: a review in the context of tour planning," Information Technology & Tourism, Springer, vol. 17(4), pages 429-453, December.
    6. Cristina Franciele & Thays Christina Domareski Ruiz, 2021. "Using sentiment analysis in tourism research: A systematic, bibliometric, and integrative review," Post-Print hal-03373984, HAL.
    7. 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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