Year: 2024
Type: article
Abstract: This paper explores international tourism regarding Morocco’s leading touristic city Marrakech, and, more precisely, its two prominent public spaces, Jemaa el-Fna and the Medina. Following a web-scraping process of English reviews on TripAdvisor, a machine learning technique is proposed to gather insights into prominent topics in the data, and their corresponding sentiment with a specific voting model. This process allows decision makers to direct their focus onto certain issues, such as safety concerns, animal conditions, health, or pricing issues. In addition, the voting method outperforms Vader, a widely used sentiment prediction tool. Furthermore, an LLM (Large Language Model) is proposed, the SieBERT-Marrakech. It is a SieBERT model fine-tuned on our data. The model outlines good performance metrics, showing even better results than GPT-4o, and it may be an interesting choice for tourism sentiment predictions in the context of Marrakech.
Source: Stats
DOI: 10.3390/stats7040090
Authors: Kenza Charfaoui, Stéphane Mussard
Institutions: Université Mohammed VI Polytechnique, Université de Nîmes
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,819
Funder: Agence Nationale de la Recherche
Award: GARDENER
OpenAlex ID: W4405737688