Covid 19 and lodging places

[EN] Tourism is a very important source of income for national economies all over the world. Before Covid-19, this sector contributed with 10.4% of the global GDP. Innovative tools for tourism study and promotion are very necessary for a future recovery of the industry. Thus, we have explored Airbnb data as a source of information about the lodging sector, very relevant within the tourism industry. We have analyzed these data to explore the experience of tourists before and after the pandemic. Our aims included identifying and visualizing opinion changes through semantics extracted from semi-structured data generated by the Airbnb customers. We used Natural Language Processing and techniques such as sentiment analysis combined with spatial analysis with KDE in order to characterize and spatially visualize user opinion. Results did not show significant differences in user opinion before and after the outbreak of Covid, however spatial patterns related to sentiments were made visible. Moreover, a large dataset covering 3.6M Airbnb lodging spots from 108 cities was compiled and will be made available in the future. This paper can be useful for the lodging industry, tourism organizations as well as social media researchers by providing an alternative approach that involves the role of location in the study of customer behaviour.Ruiz-Martinez, E.; Porras-Bernardez, F.; Gartner, G. (2022). Covid 19 and lodging places. En 4th International Conference on Advanced Research Methods and Analytics (CARMA 2022). Editorial Universitat Politècnica de València. 237-244. https://doi.org/10.4995/CARMA2022.2022.1509823724

WordCrowd : a location-based application to explore the city based on geo-social media and semantics

WordCrowd is a dynamic location-based service that visualizes and analyzes geolocated social media data. By spatially clustering the data, areas of interest and their descriptions can be extracted and compared on different geographical scales. When walking through the city, the application visualizes the nearest areas of interest and presents these in a word cloud. By aggregating the data based on the country of origin of the original poster, we discover differences and similarities in tourist interest between different countries. This work is part of the project Eureca: European Region Enrichment in City Archives and Collections of Ghent University (IDLab, CartoGIS), the Technical University of Vienna (Research Group Cartography) and several city and state archives from Ghent and Vienna.(VLID)452639