Persona and issue analysis on tourism social media: a case study of Pirenópolis, Goiás, Brazil

Context: In 2022, tourism earned R$208 billion, 28% more than in 2021, indicating a growing interest on the part of travelers in practical experiences during their trips, as well as creating content such as opinions, ratings, and recommendations. This has led both the public and private sectors to direct their efforts to improve service quality. Problem: Due to the large data volumes, discovering the tourist profile manually becomes impractical, making it imperative to adopt knowledge discovery techniques that automate this process. Solution: We present a solution for identifying the tourist profiles through persona analysis, using hotels from Pirenópolis in Goiás, Brazil, as a case study. IS Theory: The work was developed using the Social Media Engagement Theory about content generated by users on TripAdvisor and Booking.com platforms due to users experiencing a feeling of community, aligning with the central idea of theory highlighting the importance of active engagement. Method: In this study, we adopted the Cross-Industry Standard Process for Data Mining method, a consolidated approach in data mining. Using this method, we conducted exploratory analysis, profile analysis, topic modeling, and the grouping technique, enabling us to prepare a report based on the analyzed data. Summary of Results: The analysis of the topics resulted in identifying ten topics that reveal the preferences and needs of travelers. The application of the grouping technique allowed the creation of personas based on this data, enriching the understanding of the user profile. This results in a purposeful report on products and services best adapted to travelers’ needs. Contributions and Impact in IS area: Facing challenges in systems in the era of innovation based on connected open data, this work contributes to this discussion as it dynamically fits, providing an innovative and practical approach to dealing with expanding information, automating data analysis processes, and increasing the operational efficiency of tourism companies in forming strategies to enhance decision-making.