In the realm of smart tourism, understanding tourist feedback through sentiment analysis is pivotal for enhancing service quality and experience. This study introduces a novel Hybrid Transformer-Attention Model (HTAM), designed to advance sentiment classification accuracy, model interpretability, and linguistic adaptability in tourist reviews. By integrating the contextual insights of transformer models with a strategic attention mechanism, HTAM aims to provide superior performance over existing hybrid models like Convolutional Neural Networks—Bidirectional Long Short-Term Memory (CNN-BiLSTM) (Meng, The Convolutional Neural Network Text Classification Algorithm in the Information Management of Smart Tourism Based on Internet of Things, 2024). With empirical evidence showcasing its capability to outperform conventional models in metrics with an accuracy of 96.74%, HTAM’s application promises to revolutionize smart tourism by enabling more responsive and tailored service improvements.