# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 3 Materials and Methods
### Hybrid Random Walk-Optimized Simulated Annealing for Tourism Sentiment Chain Modeling
#### 3.3.2 Tourism Sentiment Chain Generation Method  
The RW-OSA-TSCM approach enables multidimensional association-driven travel route recommendations by integrating a dynamic weight adjustment mechanism with a global optimization strategy. Specifically, this methodology is executed through two critical steps: generating random walk paths and optimizing these paths via an advanced simulated annealing algorithm, as shown in Figure 3. In path generation, Attraction Transition probabilities are dynamically computed using weighted SP, RC, and TS metrics, simulating tourist decision-making processes and forming a synthetic path pool. The Comprehensive Attractiveness Index (CAI), derived from selection frequencies, quantifies the likelihood of preference across attractions. During optimization, the objective function maximizes the cumulative CAI along a route. Leveraging a multi-neighborhood perturbation scheme and adaptive cooling schedule, the algorithm performs a global search to identify the optimal Tourism Service Chain (TSC). (1) Path Generation via Random Walk  
An initial travel itinerary is generated using a random walk (RW) process initiated from a selected attraction. At each step, the transition probability between nodes is calculated by integrating three interrelated dimensions weighted by adaptive coefficients:  
\[prob_{ij}^{(t)}=\alpha^{(t)}\cdot SP_{ij}+\beta^{(t)}\cdot RC_{ij}+\gamma^{(t)} \cdot TS_{ij}, \tag{15}\]  
where \(\alpha^{(y)}\), \(\beta^{(y)}\), and \(\gamma^{(y)}\) are, respectively, the weight values of the three kinds of correlation relationships when choosing the \(t\)-th attraction during a random walk, and their initial weight values are derived from tourist preference profiles or historical mobility patterns, constrained so that \(\alpha+\beta+\gamma=1\). \(SP_{ij}\), \(RC_{ij}\), and \(TS_{ij}\) represent the quantitative measures of inter-attraction relationships introduced in Section 3.2. The model dynamically adjusts these weights based on the dominant influence at each transition. The prevailing factor is determined by the following expression:  
\[force_{ij}^{(t)}=\text{argmax}\Big{\{}\alpha^{(t)}\cdot SP_{ij},\beta^{(t)} \cdot RC_{ij},\gamma^{(t)}\cdot TS_{ij}\Big{\}}. \tag{16}\]  
For instance, if \(SP\) emerges as the leading factor, the weights are recalibrated as follows:  
\[\hat{\alpha}=\alpha\times\delta,\ \hat{\beta}=\beta+\varepsilon,\gamma=\gamma+\varepsilon, \tag{17}\]  
where \(\delta\) acting as a decay parameter and \(\varepsilon\) serving as a redistribution coefficient. The updated weights are subsequently normalized to maintain unit sum. Similar update mechanisms apply when RC or TS prevail, thereby enabling real-time adaptation to shifting visitor preferences. Through multiple RW iterations, a diversified set of candidate itineraries is constructed into a path repository. A Comprehensive Attractiveness Index (CAI) is then derived by computing the relative frequency of transitions between attraction pairs:  
\[CAI_{ij}=\frac{Count(i\to j)}{\sum_{k\in N(i)}Count(i\to k)}, \tag{18}\]  
Figure 3: Tourism Sentiment Chain generation method. where \(Count(i\to j)\) denotes the number of times \(j\) follows \(i\) in the path repository, and \(N(i)\) represents the ensemble of all paths involving \(i\). Path Optimization via Optimized Simulated Annealing  
To mitigate premature convergence in conventional route planning and elevate the global quality of Tourism Sentiment Chains, the Optimized Simulated Annealing (OSA) is proposed, which combines a dynamic temperature control scheme with a multi-neighborhood perturbation strategy to iteratively refine candidate itineraries derived from random walks. Within the OSA framework, path quality is quantified using an objective function built upon the Comprehensive Attractiveness Index (CAI). A multi-mode neighborhood search is introduced to balance exploration and exploitation, thereby enhancing both solution diversity and local refinement.