Abstract

The exponential growth of digital tourism data presents both opportunities and challenges for creating personalized travel experiences. This paper evaluates various Natural Language Processing (NLP) techniques for developing smart tourism profiling systems that transform unstructured tourist data into actionable insights. We systematically analyze sentiment analysis, topic modeling, named entity recognition, and deep learning approaches applied to diverse tourism datasets including social media posts, reviews, and travel blogs. Our proposed hybrid framework combines transformer-based models with traditional machine learning techniques to create comprehensive tourist profiles. The experimental evaluation demonstrates that our approach achieves 89.3% accuracy in tourist preference classification and 85.7% precision in destination recommendation tasks. We implemented a multi-modal architecture processing over 500,000 tourism-related documents across five languages, revealing significant improvements over baseline methods. The system successfully identifies tourist behavioral patterns, preferences, and sentiment distributions with real-time processing capabilities. Key findings indicate that BERT-based models outperform traditional approaches by 12-15% in tourism domain-specific tasks, while our ensemble method reduces computational overhead by 23%. The research contributes to smart tourism by providing a scalable, multilingual framework for tourist profiling that enhances personalization and destination marketing strategies. Our results demonstrate the potential of NLP techniques in revolutionizing tourism analytics and supporting data-driven decision making in the hospitality industry.