The Impact of Using Deep Learning Techniques for Analyzing Tourist Reviews and Providing Personalized Recommendations: A New Study in the Field of Big Data.

Online tourism reviews are a rich source of information that contributes to tourism decision-making. Deep learning techniques are one of the most prominent areas of artificial intelligence used to analyze big data, particularly in the tourism sector. Therefore, this research aims to explore how deep learning techniques can be used to analyze online tourism reviews and leverage these analyses to provide personalized recommendations to users. The study followed an analytical methodology based on collecting and analyzing data from tourism reviews available on various platforms. Deep learning techniques, such as Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs), were used to extract patterns and trends from these reviews. Tourism review data was collected from multiple online platforms, with a focus on reviews in Arabic. The study population comprised employees at various administrative, technical, and engineering levels in various tourism locations. The data was analyzed from 350 valid questionnaires distributed. The results demonstrated that deep learning techniques are capable of extracting accurate information from tourism reviews, which helps understand tourist preferences. The proposed model was able to provide personalized recommendations to tourists based on rating analysis, resulting in a 15% increase in customer satisfaction.