Sentiment Analysis for Tourism Insights: A Machine Learning Approach

Kenza Charfaoui and Stéphane Mussard ()
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Kenza Charfaoui: Faculty of Governance, Economics and Social Sciences, Mohammed VI Polytechnic University, Rabat 11100, Morocco
Stéphane Mussard: Faculty of Governance, Economics and Social Sciences, Mohammed VI Polytechnic University, Rabat 11100, Morocco

Stats, 2024, vol. 7, issue 4, 1-13

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.

Keywords: Marrakech; large language models; machine learning; sentiment analysis; voting model (search for similar items in EconPapers)
JEL-codes: C1 C10 C11 C14 C15 C16 (search for similar items in EconPapers)
Date: 2024
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