Sketch of a Decision-Support System to Improve the Management of Tourism Destinations

User feedback is a crucial indicator of the success of any tourist destination, as it reflects the level of satisfaction with the services provided in that destination. This paper outlines a deep learning-based approach to creating a decision support system that can help tourism managers in Morocco make better choices to maintain the region’s competitiveness and attract more visitors. Our proposed method uses deep learning architectures to analyze tourist feedback and evaluate three factors that define a good tourist destination, including value for money, cleanliness, and staff. Two common natural language processing (NLP) tasks - TC (Text classification) and SA (Sentiment Analysis) are used to classify comments and assess tourist satisfaction. Thanks to this approach, tourism managers will be able to identify specific areas requiring improvement to better meet tourists’ needs and, ultimately, increase their satisfaction.