Assessing Machine Learning Models for Enhancing Intent Detection in Tourism Chatbots

The tourism sector has recently undergone a significant transformation with the integration of chatbots, enabling users to interact with services through natural language. At the heart of these systems lies the Natural Language Understanding (NLU) component, which processes user input through intent classification and entity extraction. A major challenge, however, is selecting the most effective machine learning method to build robust NLU systems tailored to tourism applications. This study evaluates the performance of various machine learning algorithms for intent classification in tourism-focused chatbots. The models under investigation include Support Vector Machine (SVM), LightGBM, XGBoost, and Random Forest. A tourism-specific dataset was developed for this comparative analysis, with evaluation based on metrics such as accuracy and weighted F1-score. The experimental results indicate that XGBoost, LightGBM, and Random Forest achieve the highest training accuracy in intent classification. These outcomes offer valuable insights for developing effective NLU components in tourism chatbots, improving their ability to interpret user queries accurately.