# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 4 Results
### Construction and Analysis of Multi-Dimensional Correlation Network of Tourist Attractions
#### 4.2.3 Thematic Sentiment Network  
In Figure 10c, the thematic sentiment network underscores thematic coherence and emotional resonance among attractions, emphasizing the continuity and coherence of \begin{table}
\begin{tabular}{c c} \hline \hline
**Attraction** & **Composite Influence Score** \\ \hline Hankou Riverside & 0.6993 \\ East Lake & 0.5984 \\ Jianghan Road Pedestrian Street & 0.5903 \\ GuiYuan Temple & 0.5568 \\ East Lake Beach Scenic Area & 0.5509 \\ Wuhan Zoo & 0.5340 \\ GuDe Temple & 0.5233 \\ LiHuangPi Road & 0.4983 \\ Turtle Mountain & 0.4880 \\ East Lake Greenway & 0.4738 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: The top 10 attractions in terms of composite influence score in the resonance correlation network. \begin{table}
\begin{tabular}{c c} \hline \hline
**Attraction** & **Composite Influence Score** \\ \hline HongShan Square & 0.7257 \\ Chu River and Han Street & 0.6765 \\ Hubei Provincial Library & 0.6713 \\ Han Show Theater & 0.6484 \\ DaYu Mythical Garden & 0.6172 \\ HuaLou Street & 0.6067 \\ ZhongShan Avenue & 0.6047 \\ TieMenGuan & 0.5964 \\ WS Snow Wonder & 0.5929 \\ GuQin Terrace & 0.5851 \\ \hline \hline \end{tabular}
\end{table} 
Table 5: The top 10 attractions in terms of composite influence score in the spatial proximity network. tourists' emotional experiences. Table 7 identifies the top 10 attractions based on composite influence within this network. Wuhan Garden Expo Park, Hankou Riverside, and Zhongshan Park rank prominently due to their multidimensional thematic and emotional representations. High rankings of sites like Jiuzhen Mountain and Happy Jungle Cherry Blossom Theme Park indicate a prevalence of natural landscapes and leisure activities as dominant themes across Wuhan. Nevertheless, attractions such as Jiuzhen Mountain, Jinligou, and Yaojia Mountain, owing to their remote geographical locations, may not be ideal candidates for unconditional inclusion in tourism route recommendations. Thus, while thematic sentiment connections effectively capture experiential consistency among attractions, they should not serve as the sole criterion for tourism route planning. High average degree centrality and global clustering coefficients, combined with exceptionally short average shortest path lengths, demonstrate a densely interconnected network structure. Low modularity and degree distribution power-law exponents indicate ambiguous community boundaries, characterized by substantial overlap and thematic-emotional similarities across attractions, with indistinct divisions between thematic categories. A comparative structural analysis of the three networks reveals several key insights. The spatial proximity network demonstrates limited connectivity, constraining its applicability in modeling cross-regional tourist behavior. While the resonance correlation network captures subjective cognitive patterns among tourists, its sparse connectivity hampers performance in large-scale recommendation tasks. Conversely, the thematic sentiment network excels in generating immersive and emotionally coherent travel routes, offering distinct advantages in affective sequence modeling and personalized recommendation systems. Notably, the geographic constraints embedded in the spatial proximity network provide a foundational framework for the resonance correlation network, ensuring that cognitive associations remain grounded in real-world transportation feasibility. Furthermore, the high connectivity of the thematic sentiment network effectively bridges the 12 disconnected subgraphs identified in the spatial proximity network through thematic-emotional similarity, thereby introducing affective drivers into cross-regional route formation. Crucially, the interplay between the resonance correlation network's individual-level cognitive preferences and the thematic sentiment network's population-level emotional consensus establishes a complementary micro-macro analytical framework.