Advancing smart tourism destinations: A case study using bidirectional encoder representations from transformers‐based occupancy predictions in torrevieja (Spain) - José Ginés Giménez Manuel; José Giner Pérez de Lucia; Marco Antonio Celdrán Bernabeu; José Norberto Mazón López; Juan Carlos Cano Escribá; José María Cecilia Canales (IET Smart Cities)
Abstract Tourism represents a crucial socio‐economic pillar globally, yet the multifaceted challenges it poses necessitate innovative management approaches. The paradigm of smart tourism harnesses advanced data analytics tools to promote both profitability and sustainability in tourist destinations, leading to new levels of destination smartness. Accurate tourist occupancy prediction, particularly in areas dominated by second‐home accommodations where traditional hospitality data may be insufficient, plays a key role in optimising tourism management. To address this data gap, our prior research employed ARIMA modelling on Airbnb booking time series and analysed tourism‐related Twitter conversations to forecast occupancy levels in Torrevieja (Alicante); a prominent second‐home tourism destination in Southeastern Spain. In this extended study, we delve deeper into the realm of social sensing by utilising bidirectional encoder representations from transformers (BERT) for topic modelling. Our methodology involves the processing and analysis of Twitter data to identify prominent themes related to Torrevieja. The findings not only reveal nuanced perceptions and discussions about the destination but also underscore the effectiveness of BERT in capturing intricate topic dynamics. Importantly, this work highlights how the alignment of specific topics with booking patterns can further enhance predictive accuracy for tourist occupancy, presenting a robust toolkit for stakeholders in the tourism sector.