# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 4 Results
### Tourism Sentiment Chain Generation and Scenario Validation
#### 4.4.2 Tourism Sentiment Chain Realization and Comparative Analysis  
Following the procedure outlined in Section 3.3.2 (2), we configured the simulated annealing parameters with an initial temperature of 5, a cooling rate of 0.95, and 1000 iterations. This setup ensures strong global exploration in the early stages of optimization while allowing gradual convergence toward locally optimal solutions. Given that short-distance itineraries typically encompass 4-8 attractions and considering the CAI network diameter of 8, we employed the OSA method to generate TSCs of fixed length 8. Taking the Tourism Emotional Chain starting from the Yellow Crane Tower as an example, as shown in Figure 12, it can be observed that the TSC exhibits certain spatial continuity, ensuring its accessibility. The CAI between adjacent scenic spots within this TSC, along with their rankings among all potential options, underscores the holistic integration of multi-dimensional associations, as elaborated in Table 9. Centered around the Yellow Crane Tower, the entire route exhibits pronounced geographic proximity, exemplified by connections like \"Yellow Crane Tower \(\rightarrow\) Shouyi Park\" and \"Turtle Mountain \(\rightarrow\) Tiejamen Pass\", which demonstrate excellent geographical accessibility. Approximately half of the neighboring attractions exhibit notable resonance correlation, while several segments show high thematic sentiment similarity, such as \"Shouyi Park \(\rightarrow\) Ziyang Park\" and \"Jianghan Road Pedestrian Street \(\rightarrow\) Hankou Main Street\". This coherence fosters enhanced psychological engagement and identification among tourists. Figure 12: Tourism Sentiment Chain of Yellow Crane Tower. Unlike routes based solely on one type of association, this itinerary integrates multi-dimensional CAI metrics, offering tourists a rich tapestry of interconnected experiences at various stages. Starting from the Yellow Crane Tower, the TSC progressively transitions from historical gravitas to nostalgic commercial reminiscence. The route strikes a balance between well-known and niche attractions, encompassing iconic landmarks like the Yellow Crane Tower and Jianghan Road alongside lesser-known but culturally significant sites such as Tiejiamen Pass and Turtle Mountain. The itinerary is structured logically, flowing through historical landmarks \(\rightarrow\) park green spaces \(\rightarrow\) mountain vistas \(\rightarrow\) ancient city gates \(\rightarrow\) commercial streets, creating a coherent and engaging tourist pathway with distinct thematic transitions. To assess the efficacy of the proposed TSC in accounting for route distances between attractions, the intensity of tourist associative connections, and the smooth transition of thematic elements, a comparative analysis was conducted. The adjacent attractions recommended by the TSC were juxtaposed against those suggested by leading tourism platforms, specifically Qunar ([[https://flight.qunar.com/](https://flight.qunar.com/)]([https://flight.qunar.com/](https://flight.qunar.com/))) and Mafengwo ([[https://www.mafengwo.cn/](https://www.mafengwo.cn/)]([https://www.mafengwo.cn/](https://www.mafengwo.cn/))). This comparison aimed to validate the TSC's capability to optimize itinerary planning by balancing geographical contiguity, cognitive resonance among tourists, and thematic coherence across different attractions. To evaluate geographical accessibility, we compared the spatial distances between recommended attractions generated by the TSC and those suggested by popular tourism platforms, Qunar and Mafengwo. As depicted in Figure 13, shorter spatial distances indicate higher accessibility. The TSC-recommended scenic spots have an average spatial distance of 2435 m, markedly superior to Qunar's 4472 m and Mafengwo's 2814 m. Furthermore, 69.38% of the TSC-recommended scenic spots fall within a 3 km radius, significantly exceeding Qunar's 24.68% and Mafengwo's 59.23%.