| 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. |