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Sentiment Analysis: Extracting Decision-Relevant Knowledge from UGC

In: Information and Communication Technologies in Tourism 2014

Author

Listed:
  • Sergej Schmunk

    (University of Applied Sciences Ravensburg-Weingarten)

  • Wolfram Höpken

    (University of Applied Sciences Ravensburg-Weingarten)

  • Matthias Fuchs

    (Mid-Sweden University)

  • Maria Lexhagen

    (Mid-Sweden University)

Abstract

Electronically available user generated content (UGC) dramatically increased in recent years and constitutes a highly relevant information source not only for other customers but also for tourism suppliers. Customer needs and their perception of consumed products can be extracted from UGC and represent a valuable input to product enhancement and customer relationship management. A prerequisite to that end is an automatic extraction of decision-relevant knowledge from UGC with a sufficient quality. This paper presents a novel approach for extracting decision-relevant knowledge from UGC and compares different underlying data mining techniques concerning their accuracy in topic and sentiment detection of textual user reviews. The complete extraction process is implemented and evaluated in the context of the Swedish mountain tourism destination Åre.

Suggested Citation

  • Sergej Schmunk & Wolfram Höpken & Matthias Fuchs & Maria Lexhagen, 2013. "Sentiment Analysis: Extracting Decision-Relevant Knowledge from UGC," Springer Books, in: Zheng Xiang & Iis Tussyadiah (ed.), Information and Communication Technologies in Tourism 2014, edition 127, pages 253-265, Springer.
    • Handle: RePEc:spr:sprchp:978-3-319-03973-2_19
    DOI: 10.1007/978-3-319-03973-2_19
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    Citations

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    Cited by:

    1. Selena Aureli & Enrico Supino, 2015. "Web reputation and performance measurement systems in the hotel industry: An exploratory study in Italy," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2015(2), pages 41-64.
    2. 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.
    3. Martin-Domingo, Luis & Martín, Juan Carlos & Mandsberg, Glen, 2019. "Social media as a resource for sentiment analysis of Airport Service Quality (ASQ)," Journal of Air Transport Management, Elsevier, vol. 78(C), pages 106-115.
    4. Elena Not, 2021. "Mining mobile application usage data to understand travel planning for attending a large event," Information Technology & Tourism, Springer, vol. 23(3), pages 291-325, September.
    5. Cheng Chai & Yao Song & Zhenzhen Qin, 2021. "A Thousand Words Express a Common Idea? Understanding International Tourists’ Reviews of Mt. Huangshan, China, through a Deep Learning Approach," Land, MDPI, vol. 10(6), pages 1-15, May.
    6. Saura, Jose Ramon & Palacios-Marqués, Daniel & Ribeiro-Soriano, Domingo, 2023. "Exploring the boundaries of open innovation: Evidence from social media mining," Technovation, Elsevier, vol. 119(C).
    7. 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.

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