# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 3 Materials and Methods
### Representation and Construction of Tourist Attraction Multidimensional Association Network  
Existing studies on tourist attraction associations predominantly focus on singular dimensions, resulting in a disconnection between spatial and semantic data. This fragmentation limits the ability to fully capture the intricate patterns driving tourist decision-making processes, thereby constraining insights into the dynamic and multifaceted nature of tourist behavior. To bridge this gap, we propose and construct the Multidimensional Association Network for Tourist Attractions (MAN-SRT). By integrating spatial adjacency, explicit associative co-occurrences, and implicit thematic-sentiment similarities, MAN-SRT constructs a three-dimensional relational network that encompasses spatial, associative, and thematic-sentiment dimensions. This framework enables the quantitative characterization of comprehensive inter-attraction relationships, offering a solid theoretical basis for optimizing travel itineraries and predicting tourist behaviors. The formal definition of MAN-SRT is as follows:  
\[ \begin{gathered} G=(V,E_{s},E_{R},E_{T},W),\\ V=\{Attr_{1},Attr_{2},\cdots,Attr_{i},\cdots,Attr_{n}\},\\ W=\{e_{ij}\},\hskip 14.226378 pti,j=1,2,3,\cdots,n,\\ e_{ij}=[SP_{ij},RC_{ij},TS_{ij}],\end{gathered}  \tag{10}\]  
where \(V\) is the set of tourist attractions. \(E_{s}\), \(E_{R}\), and \(E_{T}\) are the set of spatial, associative, and thematic-sentiment relationships, respectively. \(W\) is the weight matrix whose weight value \(e_{ij}\) represents the weight value of attractions \(i\) and \(j\) in terms of three-dimensional associative relationship. To address the limitations of uni-dimensional association analysis, we propose distinct strength quantification models for spatial, associative, and thematic-sentiment relationships to facilitate the synergistic computation of multidimensional associations. Specifically, spatial relationships reflect the spatial correlations required for tourists to move between different attractions, including road distance and transit time. Leveraging the gravity  
Figure 2: Flowchart of fine-grained semantic parsing method. models [40] and core tenets of spatial econometrics, the spatial proximity (SP) quantifies the geographical adjacency of attractions by integrating transit costs with a decay function:  
\[ \begin{split} SP_{ij}=\begin{cases}NORM\left(\frac{1}{Cost_{ij}} \right),\;\;Cost_{ij}<\theta_{sp},\\ 0,\;\;Cost_{ij}\geq\theta_{sp}\end{cases}\\ Cost_{ij}=\min_{me\left\{\text{bulk},bus,drive\right\}}\left(D_{ij}^{(m)} \times T_{ij}^{(m)}\right),\end{split}  \tag{11}\]  
where \(i\) and \(j\) are attraction identifiers. \(Cost_{ij}\) is the minimum transit cost between attractions. \(\theta_{sp}\) is the transit cost threshold. \(m\) is the travel mode, including three types: _walk_, _bus_, and _drive_. \(D_{ij}^{(m)}\) is the road distance between attractions under \(m\)-mode. \(T_{ij}^{(m)}\) is the travel time between attractions under \(m\)-mode. Associative relationships represent explicit semantic linkages formed when tourists mention, compare, or recommend additional attractions within online reviews. For example, in a review of Hankou Riverside that states, \"It is just sightseeing with lights; all the mentioned attractions were visited, but without any explanation, I only recognized Yellow Crane Tower and Yangtze River Bridge myself, and I personally think the ticket price is not worth it,\" two attractions--Yellow Crane Tower and Yangtze River Bridge--are mentioned. Therefore, an associative relationship is considered to exist among these three attractions (Hankou Riverside, Yellow Crane Tower, and Yangtze River Bridge). Rooted in the fundamental concepts of Apriori association rule mining [41; 42], the resonance correlation (RC) quantifies the strength of semantic associations formed through co-occurrence or linkage in tourist reviews by calculating support and itemset frequency:  
\[ \begin{split} RC_{ij}=\frac{S_{ll}}{S_{l}},\\ S_{I}=\frac{NA}{NA},\end{split}