Enhancing Multimodal Tourism Review Sentiment Analysis Through Advanced Feature Association Techniques

The development of tourism services presents significant opportunities for extracting and analyzing customer sentiment. However, with the advent of multimodality, travel reviews have brought new challenges. Early methods for detecting such reviews merely combined text and image features, resulting in poor feature correlation. To address this issue, our study proposes a novel multimodal tourism review sentiment analysis method enhanced by relevant features. Initially, we employ a fusion model that combines BERT and Text-CNN for text feature extraction. This approach strengthens semantic relationships and filters noise effectively. Subsequently, we utilize ResNet-51 for image feature extraction, leveraging its ability to learn complex visual representations. Additionally, integrating an attention mechanism further enhances modality correlation, thereby improving fusion effectiveness. On the Multi-ZOL dataset, our method achieves an accuracy of 90.7% and an F1 score of 90.8%. Similarly, on the Ctrip dataset, it attains an accuracy of 83.6% and an F1 score of 84.1%.