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Applied Data Science in Tourism

Interdisciplinary Approaches, Methodologies, and Applications

  • Textbook
  • © 2022

Overview

Editors:
  1. Roman Egger

    1. Innovation & Management in Tourism, Salzburg University of Applied Sciences, Urstein (Puch), Austria

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  • Presents the latest approaches like machine learning, text analysis, network analysis, agent based modeling
  • Includes useful “how-to" and “fact-sheet” sections with each chapter
  • Examines possible applications and uses within the field of tourism research

Part of the book series: Tourism on the Verge (TV)

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Table of contents (26 chapters)

  1. Front Matter

    Pages i-lix
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  2. Theoretical Fundaments

    1. Front Matter

      Pages 1-1
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    2. AI and Big Data in Tourism

      • Luisa Mich
      Pages 3-15

    3. Epistemological Challenges

      • Roman Egger, Joanne Yu
      Pages 17-34

    4. Data Science and Interdisciplinarity

      • Roman Egger, Joanne Yu
      Pages 35-49

    5. Data Science and Ethical Issues

      • Roman Egger, Larissa Neuburger, Michelle Mattuzzi
      Pages 51-66

    6. Web Scraping

      • Roman Egger, Markus Kroner, Andreas Stöckl
      Pages 67-82

  3. Machine Learning

    1. Front Matter

      Pages 83-83
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    2. Machine Learning in Tourism: A Brief Overview

      • Roman Egger
      Pages 85-107

    3. Feature Engineering

      • Pablo Duboue
      Pages 109-127

    4. Clustering

      • Matthias Fuchs, Wolfram Höpken
      Pages 129-149

    5. Dimensionality Reduction

      • Nikolay Oskolkov
      Pages 151-167

    6. Classification

      • Ulrich Bodenhofer, Andreas Stöckl
      Pages 169-208

    7. Regression

      • Andreas Stöckl, Ulrich Bodenhofer
      Pages 209-229

    8. Hyperparameter Tuning

      • Pier Paolo Ippolito
      Pages 231-251

    9. Model Evaluation

      • Ajda Pretnar Žagar, Janez Demšar
      Pages 253-274

    10. Interpretability of Machine Learning Models

      • Urszula Czerwinska
      Pages 275-303

  4. Natural Language Processing

    1. Front Matter

      Pages 305-305
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    2. Natural Language Processing (NLP): An Introduction

      • Roman Egger, Enes Gokce
      Pages 307-334

    3. Text Representations and Word Embeddings

      • Roman Egger
      Pages 335-361
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Keywords

About this book

Access to large data sets has led to a paradigm shift in the tourism research landscape. Big data is enabling a new form of knowledge gain, while at the same time shaking the epistemological foundations and requiring new methods and analysis approaches. It allows for interdisciplinary cooperation between computer sciences and social and economic sciences, and complements the traditional research approaches. This book provides a broad basis for the practical application of data science approaches such as machine learning, text mining, social network analysis, and many more, which are essential for interdisciplinary tourism research. Each method is presented in principle, viewed analytically, and its advantages and disadvantages are weighed up and typical fields of application are presented. The correct methodical application is presented with a "how-to" approach, together with code examples, allowing a wider reader base including researchers, practitioners, and students entering the field. 

The book is a very well-structured introduction to data science – not only in tourism – and its methodological foundations, accompanied by well-chosen practical cases. It underlines an important insight: data are only representations of reality, you need methodological skills and domain background to derive knowledge from them


- Hannes Werthner, Vienna University of Technology

 
Roman Egger has accomplished a difficult but necessary task: make clear how data science can practically support and foster travel and tourism research and applications. The book offers a well-taught collection of chapters giving a comprehensive and deep account of AI and data science for tourism


- Francesco Ricci, Free University of Bozen-Bolzano
 
This well-structured and easy-to-read book provides a comprehensive overview of data science in tourism. It contributes largely to the methodological repository beyond traditional methods.


- Rob Law, University of Macau




Reviews

“Applied Data Science in Tourism is an incredibly helpful contribution to data science research in the fields of tourism and hospitality that is both easy to read and tremendously fascinating. This book is a valuable source of theoretical and methodological knowledge for both academics and industry practitioners of tourism and related fields such as hospitality, leisure, and event management. Especially considering the increasing demand for data analytics and the use of big data in service industries.” (Omid Oshriyeh, Information Technology & Tourism, Vol. 25 (1), 2023)

Editors and Affiliations

  • Innovation & Management in Tourism, Salzburg University of Applied Sciences, Urstein (Puch), Austria

    Roman Egger

About the editor

Dr. Roman Egger is a full Professor at the Salzburg University of Applied Sciences at the Department of Innovation and Management in Tourism, where he is the head of eTourism, and head of key competencies and research. His research focus lies on new technologies in tourism and their adoption from a user-centric perspective, as well as on methodological issues in tourism research. Roman has published 19 books so far, a large number of articles and chapters in international journals and edited books, is co-editor of the Journal of Tourism Science (De Gruyter), series editor of “Tourism on the Verge” (Springer), and board member of a number of journals. He is a member of IFITT, AIEST, DGT, and a fellow of The ICE. Roman has received more than a dozen awards in his career.

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