MACHINE LEARNING APPLIED TO SENTIMENT ANALYSIS IN TOURISM: A SYSTEMATIC REVIEW AND FUTURE RESEARCH AGENDA
DOI:
https://doi.org/10.55028/a7r9kf38Keywords:
Tourism, UGC, Research Agenda, Artificial Intelligence, Systematic Literature ReviewAbstract
Tourism is an information-based phenomenon. Data analysis is fundamental to the sector. Machine Learning (ML) and Sentiment Analysis (SA) offer a solution for automating the analysis of user-generated content (UGC) data. In Brazil, however, the complexity of these models still limits their methodological and practical application. The relevance of this research lies in the growing use of UGC and artificial intelligence in tourism. The objective is to develop a future research agenda for the application of ML to sentiment analysis research using textual UGC data in tourism. The specific objectives are: (i) to understand the use of ML in tourism studies and (ii) to identify possible research gaps regarding the use of ML in tourism. To this end, a systematic literature review was conducted using the Scopus and Web of Science databases. Ten articles were selected for review based on a ranking using the Methodi Ordinatio. The results indicate the predominance of two types of studies: those focused on comparing and/or developing algorithms for research purposes, and those that apply ML to SA as one of their research tools. The studies also address theoretical issues, with Affective Computing standing out as a common axis. The main ML models are supervised or semi-supervised algorithms, predominantly based on Naïve Bayes or SVM, with the use of Topic Modelling or Aspect-Based Sentiment Analysis (ABSA) for sentiment mining from text. Future studies should develop more complex and contextually adapted models, as well as deepen the theoretical relationship between tourism and Affective Computing. Furthermore, it is essential that the advances achieved be disseminated both to industry professionals and to tourists, in order to enhance their practical application and promote smarter, more human experience-centered tourism.
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