Use of AI and Text Mining on Twitter for the Analysis of the Concept of Tourism in Colombia

Javier Hernández-Riaño (), Rodolfo Casadiego-Alzate (), Javier A. Sánchez-Torres (), Francisco-Javier Arroyo-Cañada (), Ana María Argila-Irurita () and María Luisa Solé-Moro ()
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Javier Hernández-Riaño: University Institution Politécnico Grancolombiano
Rodolfo Casadiego-Alzate: University Institution Politécnico Grancolombiano
Javier A. Sánchez-Torres: University of Medellin
Francisco-Javier Arroyo-Cañada: University of Barcelona-Spain
Ana María Argila-Irurita: University of Barcelona-Spain
María Luisa Solé-Moro: University of Barcelona-Spain

A chapter in Advances in Digital Marketing and eCommerce, 2024, pp 209-224 from Springer

Abstract: Abstract Tourism is an essential industry for the economic and social development of countries, especially in Latin America. Colombia is a case that has exploited the potential of tourism to generate income, employment and strengthen its operational infrastructure. Based on the above, the objective of this research is to identify the association or concept of tourism in Colombia from the mentions available on Twitter, a social network that allows to know the perception and experience of visitors. In this work, a three-step methodology was implemented: 1) Identify keywords associated with tourism that were suggested by experts and generative artificial intelligence tools; 2) Transform the keywords into hashtags and download the most relevant tweets using Scraping and 3) Analyze the tweets with natural language processing and text mining techniques to extract insights. The results show that the hashtags created to refer to tourism in Colombia can be coherently related to the campaigns that the country has developed at different times. Finally, it is concluded that the integration of AI, social networks and text mining represent an effective combination to understand and strengthen the concept of tourism in Colombia.

Keywords: Tourism; AI; Text Mining; Colombia (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:spr:prbchp:978-3-031-62135-2_22

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DOI: 10.1007/978-3-031-62135-2_22

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