Decoding Destination Image and Performance: Insights from Microblog Metrics
(A2025-124379)

AUTHORS

Ilona Pezenka, FHWien der WKW University of Applied Sciences for Management & Communication; Christian Weismayer, Modul University Vienna; Melise de Lima PEREIRA, Federal University of Paraná

ABSTRACT

This study introduces an automated text mining method to analyze the perceived image of Brazilian coastal tourism destinations based on data from X (formerly Twitter). Using a classification algorithm and sentiment analysis, the importance and performance of fourteen tourism types defined by the UNWTO is measured. The resulting metrics are then combined in a grid to visualize destination images, offering a comparative, attribute-based approach for destination management. This method provides a flexible, temporally unlimited, and destination-independent tool for analyzing and understanding destination image across various contexts.