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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.
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.