Towards a Decision Support System for Tourism Sector in Daraa-Tafilalet

Abstract

Integrating intelligent data engineering and smart applications in the tourism sector offers numerous opportunities to improve the traveler experience, optimize operations, and enhance innovation. User feedback, reflecting their level of satisfaction with the services provided, is a crucial indicator of the success of a tourist destination. In this paper, we present an approach based on artificial intelligence to create a decision support system to guide the choices of tourism sector managers in Morocco. The aim is to maintain the region's competitiveness and attract a growing number of visitors. We propose using deep learning to analyze tourists' comments and evaluate the factors characterizing a tourist destination, such as facilities, value for money, cleanliness, comfort, and staff. Our method is based on two common natural language processing (NLP) tasks: text classification to extract relevant elements and sentiment analysis to assess tourist satisfaction. This approach enables tourism managers to effectively target improvement areas to meet tourists' expectations better and increase their satisfaction.