Tourism destination events classifier based on artificial intelligence techniques

Paper: arXiv 2410.19741

Authors: Miguel Camacho-Ruiz, Ramón Alberto Carrasco, Gema Fernández-Avilés, Antonio LaTorre

Abstract

Identifying client needs to provide optimal services is crucial in tourist destination management. The events held in tourist destinations may help to meet those needs and thus contribute to tourist satisfaction. As with product management, the creation of hierarchical catalogs to classify those events can aid event management. The events that can be found on the internet are listed in dispersed, heterogeneous sources, which makes direct classification a difficult, time-consuming task. The main aim of this work is to create a novel process for automatically classifying an eclectic variety of tourist events using a hierarchical taxonomy, which can be applied to support tourist destination management. Leveraging data science methods such as CRISP-DM, supervised machine learning, and natural language processing techniques, the automatic classification process proposed here allows the creation of a normalized catalog across very different geographical regions. Therefore, we can build catalogs with consistent filters, allowing users to find events regardless of the event categories assigned at source, if any. This is very valuable for companies that offer this kind of information across multiple regions, such as airlines, travel agencies or hotel chains. Ultimately, this tool has the potential to revolutionize the way companies and end users interact with tourist events information.

Complexity vs Empirical Score

• Math Complexity: 3.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: The paper applies standard NLP and machine learning techniques (BERT and Logistic Regression) to solve a practical data engineering and classification problem in tourism destination management. While the mathematical contribution is minimal and relies on existing formulations, the empirical application leverages a large-scale real-world dataset across multiple languages.

Research Flowchart

  flowchart TD
    A["Research Goal: Automatic classification of heterogeneous tourist events into a normalized hierarchical taxonomy"] --> B["Data Input: Dispersed & heterogeneous tourist event data from internet sources"]
    B --> C["CRISP-DM Methodology: Data preprocessing & Natural Language Processing (NLP)"]
    C --> D["Supervised Machine Learning Model"]
    D --> E["Outcome 1: Standardized multi-region event catalog with consistent filters"]
    D --> F["Outcome 2: Optimized event management for airlines, travel agencies, & hotel chains"]