Discovering a tourism destination with social media data: BERT-based sentiment analysis
Metadatos
Mostrar el registro completo del ítemEditorial
Emerald
Materia
Social media Deep learning Tourism Sentiment analysis Data analysis
Fecha
2022-08-19Referencia bibliográfica
Viñán-Ludeña, M.S. and de Campos, L.M. (2022), "Discovering a tourism destination with social media data: BERT-based sentiment analysis", Journal of Hospitality and Tourism Technology, Vol. ahead-of-print No. ahead-of-print. [https://doi.org/10.1108/JHTT-09-2021-0259]
Patrocinador
Spanish Ministerio de Ciencia e Innovacion, Agencia Estatal de Investigacion PID2019-106758GB-C31; European CommissionResumen
Purpose – The main purpose of this paper is to analyze a tourist destination using sentiment analysis
techniques with data from Twitter and Instagram to find the most representative entities (or places) and
perceptions (or aspects) of the users.
Design/methodology/approach – The authors used 90,725 Instagram posts and 235,755 Twitter tweets
to analyze tourism in Granada (Spain) to identify the important places and perceptions mentioned by travelers
on both social media sites. The authors used several approaches for sentiment classification for English and
Spanish texts, including deep learning models.
Findings – The best results in a test set were obtained using a bidirectional encoder representations
from transformers (BERT) model for Spanish texts and Tweeteval for English texts, and these were
subsequently used to analyze the data sets. It was then possible to identify the most important
entities and aspects, and this, in turn, provided interesting insights for researchers, practitioners,
travelers and tourism managers so that services could be improved and better marketing strategies
formulated.
Research limitations/implications – The authors propose a Spanish-Tourism-BERT model for
performing sentiment classification together with a process to find places through hashtags and to reveal the
important negative aspects of each place.
Practical implications – The study enables managers and practitioners to implement the Spanish-BERT
model with our Spanish Tourism data set that the authors released for adoption in applications to find both
positive and negative perceptions.
Originality/value – This study presents a novel approach on how to apply sentiment analysis in
the tourism domain. First, the way to evaluate the different existing models and tools is presented;
second, a model is trained using BERT (deep learning model); third, an approach of how to identify
the acceptance of the places of a destination through hashtags is presented and, finally, the
evaluation of why the users express positivity (negativity) through the identification of entities and
aspects.