StreetLines: A Smart and Scalable Tourism Platform Based on Efficient Knowledge-Mining

Identifying and understanding visitor needs and expectations is of the utmost importance for a number of stakeholders and policymakers involved in the touristic domain. Apart from traditional forms of feedback, an abundance of related information exists online, scattered across various data sources...

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Veröffentlicht in:Digital 2024-08, Vol.4 (3), p.676-697
Hauptverfasser: Alexandridis, Georgios, Siolas, Georgios, Papagiannis, Tasos, Ioannou, George, Michalakis, Konstantinos, Caridakis, George, Karyotis, Vasileios, Papavassiliou, Symeon
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Sprache:eng
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Zusammenfassung:Identifying and understanding visitor needs and expectations is of the utmost importance for a number of stakeholders and policymakers involved in the touristic domain. Apart from traditional forms of feedback, an abundance of related information exists online, scattered across various data sources like online social media, tourism-related platforms, traveling blogs, forums, etc. Retrieving and analyzing the aforementioned content is not a straightforward task and in order to address this challenge, we have developed the StreetLines platform, a novel information system that is able to collect, analyze and produce insights from the available tourism-related data. Its highly modular architecture allows for the continuous monitoring of varying pools of heterogeneous data sources whose contents are subsequently stored, after preprocessing, in a data repository. Following that, the aforementioned data feed a number of independent and parallel processing modules that extract useful information for all individuals involved in the tourism domain, like place recommendation for visitors and sentiment analysis and keyword extraction reports for professionals in the tourism industry. The presented platform is an outcome of the StreetLines project and apart from the contributions of its individual components, its novelty lies in the holistic approach to knowledge extraction and tourism data mining.
ISSN:2673-6470
2673-6470
DOI:10.3390/digital4030034