| dc.description.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. | vi |
