# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 4 Results
### Attribute Extraction and Analysis of Tourist Attractions
#### 4.2.1 Extraction of Textual Entity Information  
The analysis of tourism review texts is conducted in three sequential steps: (i) identification and extraction of characteristic words; (ii) composition and extraction of viewpoint phrases; and (iii) thematic classification of viewpoint phrases. (1) Identification and Extraction of Characteristic Words  
Drawing from typical sentiment expression patterns observed in tourist reviews, this study categorizes characteristic words into five distinct classes: Object, Property, Degree, Action, and Other. Utilizing the BIO labeling scheme, a corpus of 3000 non-redundant reviews was compiled through stratified sampling and divided into training, validation, and test sets at a 6:2:2 ratio. Drawing on the GSPM-RTTE methodology proposed in Section 3.1.2, a BERT-BiLSTM-CRF model architecture is implemented. Empirical analysis reveals model convergence within 12-15 training epochs; hence, the training process is extended to 20 epochs to ensure full optimization. Performance evaluation is conducted on the test set, with results summarized in Table 1.  
Figure 5: Distribution of travel review text data. (**a**) Distribution of the number of reviews for tourist attractions. (**b**) Distribution of review text length. The evaluation results show that the model demonstrates strong performance in extracting all characteristic word categories, with F1-scores ranging from 0.85 to 0.96. Especially, it achieves particularly high precision (0.94-0.98) for Property, Degree, and Action types, indicating reliable identification. In contrast, the Object and Other categories exhibit relatively lower F1-scores due to the presence of specific product names related to attraction features. The balanced micro-average F1-score of 0.89 suggests robust overall performance. Precise Object and Property identification (F1 \(\geq\) 0.87) enables accurate subject-attribute pairing in viewpoint mining. 2. Composition and Extraction of Viewpoint Phrases Based on the syntactic and semantic structures of opinion phrases in travel review texts, we designed mapping rules between syntactic structures and viewpoint phrase combinations according to the GSPM-TRTE method described in Section 3.1.2. Take the result of extracting characteristic words as an external dictionary. The HanLP toolkit was employed to perform syntactic analysis on the review texts for the extraction of viewpoint phrases. The extracted results were compared with manually annotated benchmarks, and the performance on selected attraction datasets is summarized in Table 2.  
Evaluation results indicate that the model performs well on the overall dataset, effectively covering most real-world instances of opinion phrases. The F1-scores across different datasets are relatively close and all exceed 0.8, suggesting that the rule-based design is robust to imbalanced sample distributions. The weighted recall (0.9455) is slightly higher than the weighted precision (0.8949), indicating that the system prioritizes high coverage to capture as many potential opinion phrases as possible, at the cost of a small number of false positives. A weighted accuracy of 0.9190 demonstrates that the proposed HanLP-based syntactic rule approach effectively extracts tourists' opinions from travel reviews, laying a solid foundation for subsequent semantic feature analysis of scenic attractio \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Characteristic Word Type** & **Precision** & **Recall** & **F1-Score** & **Support** \\ \hline Object & 0.88 & 0.86 & 0.87 & 799 \\ Property & 0.94 & 0.91 & 0.92 & 573 \\ Degree & 0.98 & 0.94 & 0.96 & 68 \\ Action & 0.94 & 0.84 & 0.89 & 295 \\ Other & 0.89 & 0.81 & 0.85 & 21 \\ \hline micro avg & 0.91 & 0.88 & 0.89 & 1756 \\ macro avg & 0.93 & 0.87 & 0.90 & 1756 \\ weighted avg & 0.91 & 0.88 & 0.89 & 1756 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Evaluation of characteristic word entity extraction accuracy.