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Applied data science in tourism : interdisciplinary approaches, methodologies, and applications / Roman Egger, editor.

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 be...

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Bibliographic Details
Online Access: Full Text (via Springer)
Other Authors: Egger, Roman (Editor)
Format: eBook
Language:English
Published: Cham : Springer, [2022]
Series:Tourism on the verge.
Subjects:
Tourism > Research > Methodology.
Tourism > Data processing.
Table of Contents:
  • Part I: Theoretical Fundaments
  • AI and Big Data in Tourism
  • Epistemological Challenges
  • Data Science and Interdisciplinarity
  • Data Science and Ethical Issues
  • Web Scraping
  • Part II: Machine Learning
  • Machine Learning in Tourism: A Brief Overview
  • Feature Engineering
  • Clustering
  • Dimensionality Reduction
  • Classification
  • Regression
  • Hyperparameter Tuning
  • Model Evaluation
  • Interpretability of Machine Learning Models
  • Part III: Natural Language Processing
  • Natural Language Processing (NLP): An Introduction
  • Text Representations and Word Embeddings
  • Sentiment Analysis
  • Topic Modelling
  • Entity Matching: Matching Entities Between Multiple Data Sources
  • Knowledge Graphs
  • Part IV: Additional Methods
  • Network Analysis
  • Time Series Analysis
  • Agent-Based Modelling
  • Geographic Information System (GIS)
  • Visual Data Analysis
  • Software and Tools.

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