# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 3 Materials and Methods
### Spatial-Semantic Integrated Model for Tourist Attraction Representation
#### 3.1.1 Spatial and Semantic Feature Representation of Tourist Attraction Entity  
When planning a trip, tourists typically consider a range of factors such as the unique offerings, optimal visiting time, geographical accessibility, and peer-generated reviews of attractions. To holistically model the multifaceted influences in tourism decision-making, this study formulates the feature representation of tourist attractions as follows:  
\[Attr=\{\mathit{BasServ},\mathit{SptLoc},\mathit{Theme},\mathit{ Emotion}\}, \tag{1}\]  
where \(Attr\) is the attraction entity. \(\mathit{BasServ}\) is the basic information of attraction, such as the name of the attraction, opening time, official phone number, preferential policies, etc. \(\mathit{SptLoc}\) is the spatial features of attraction. \(\mathit{Theme}\) is the thematic features of attraction. \(\mathit{Emotion}_{i}\) is the sentiment features of attraction. Figure 1: Flowchart of the research methodology. 1. Tourist attraction spatial feature representation The spatial location of an attraction critically influences its accessibility and the rationality of a tourist's itinerary, ultimately shaping the overall experiential quality. The spatial features characterize an attraction's geographic location and surrounding environment. Formally, the spatial feature \(SptLoc\) is defined as follows: \[SptLoc=\{SptCoor,\ SptRange,SptAdr,\ TopoCon,\ TopoSla\},\] (2) where \(SptCoor\) is the spatial coordinate, which indicates the center point position of the attraction. \(SptRange\) delineates the geographical boundaries of the attraction using a sequence of coordinates. \(SptAdr\) is the detailed address of the attraction, which describes the attraction's position in natural language. \(\ TopoCon\) and \(\ TopoSla\) denote the subordination or inclusion of the attraction in terms of topology or management pattern. 2. Tourist attraction thematic feature representation Tourist choice behavior is shaped not only by objective factors such as geographical accessibility and service offerings but also by deeper subjective considerations including cultural significance and thematic identity. Conventional approaches, however, often reduce thematic attributes to single labels (e.g., \"natural landscape\" or \"historical site\"), overlooking the nuanced interplay of multi-layered cultural contexts and thematic diversity. To comprehensively characterize the thematic profile of an attraction and reflect the multidimensional quality of visitor experience, we model thematic features as a distribution over theme categories along with associated weight vectors. Formally, the thematic feature _Theme_ is defined as follows: \[ \begin{array}{c}\mathit{Theme}=\{\mathit{Topic},\mathit{dist}\},\\ \mathit{Topic}=\left[T^{(1)},T^{(2)},\cdots,T^{(x)},\cdots,T^{(n_{t})}\right],\\ \mathit{dist}=\left[d^{(1)},d^{(2)},\cdots,d^{(x)},\cdots,d^{(n_{t})}\right],\end{array} \] (3) where \(n_{t}\) is the number of topic types of the viewpoint phrase. \(\mathit{Topic}\) is the theme category vector, and each element \(T^{(x)}\) represents a specific theme category. \(\mathit{dist}\) is the Thematic Distribution Vector (TDV), and each element \(d^{(x)}\) is the relative distribution weight of the theme corresponding to the theme category \(T^{(x)}\). To enable characterization and comparative analysis of core attraction attributes and corresponding tourist feedback patterns, the thematic distribution vector is computed as follows: \[ \begin{array}{c}\mathit{dist}_{i}=\frac{\mathit{D}p_{i}}{\left\|\mathit{D}p_{ i}\right\|},\\ \mathit{D}p_{i}=\left[dp_{i}^{(1)},\mathit{dp}_{i}^{(2)},\cdots,dp_{i}^{(x)}, \cdots,dp_{i}^{(n_{t})}\right],\\ \mathit{dp}_{i}^{(x)}=\frac{\mathit{v}n_{i}^{(x)}}{\mathit{v}n_{i}}/\frac{ \mathit{V}N^{(x)}}{\mathit{V}N},\end{array} \] (4) where \(i\) is the attraction identifier. \(\mathit{D}p_{i}\) denotes the relative frequency vector of themes, with each element \(dp_{i}^{(x)}\) representing the proportion of theme \(T^{(x)}\) within the overall thematic profile.