# Tourism Sentiment Chain Representation Model and Construction from Tourist Reviews
## 3 Materials and Methods
### Spatial-Semantic Integrated Model for Tourist Attraction Representation
#### 3.1.2 Granular Semantic Parsing Model for Tourism Review Textual Entity  
Semantic features of tourist attractions (service quality and pricing, etc.) constitute fundamental decision-making factors in itinerary planning and exhibit synergistic interactions with spatial characteristics like proximity and accessibility. Tourist reviews represent a rich source of such semantic insights. However, their inherently unstructured and fragmented nature requires advanced text analytics to extract granular thematic and affective signals. Conventional topic modeling and sentiment classification approaches predominantly operate at the review level, failing to capture nuanced opinion-level semantics (such as sentiment toward ticket price, tour guide performance, or recreational offerings). This results in an incomplete understanding of how thematic relevance interacts with emotional valence. To bridge this gap, we propose the Granular Semantic Parsing Model for Tourism Review Textual Entities (GSPM-TRTE). Leveraging the morphological and syntactic structures of viewpoint phrases, this model facilitates fine-grained characterization of both thematic and affective attributes of attractions. By formalizing the syntactic patterns in tourist reviews, we define three core textual entities that convert raw review content into a structured, hierarchical representation, enabling systematic semantic parsing and downstream analysis. Characteristic Words are key lexical terms that describe attraction attributes or tourist experiences, including service offerings, tourist behaviors, and intensity of emotional responses. Specifically, the characteristic word entity is defined as follows:  
\[Word=\{WName,WPos,WType\}, \tag{7}\]  
where \(Word\) is the characteristic word entity. \(WName\) is the content of the characteristic word. \(WPos\) is the part of speech of the characteristic word. \(WType\) is the type of the characteristic word. Viewpoint phrases refer to syntactically structured combinations of characteristic words that express tourists' evaluations and sentiment orientations toward specific aspects of attractions. These phrases encapsulate both evaluative content and associated affective strength. The viewpoint phrase entity is defined as follows:  
\[View=\{Vphrase,VWords,V Topic,V Sentiment\}, \tag{8}\]  
where \(View\) is the viewpoint phrase entity. \(VPhrase\) is the content of the viewpoint phrase. \(VWords\) is the set of characteristic word entities that compose the viewpoint phrase. \(V Topic\) is the topic type of viewpoint phrase. \(VSentiment\) is the sentiment polarity of the viewpoint phrase. Comment texts represent full-sentence natural language reviews posted by users on travel platforms, encompassing one or more viewpoint phrases describing holistic travel experiences. The comment text entity is defined as follows:  
\[Comment=\{Text,\,TextTime,\,TextWords,TextViews\}, \tag{9}\]  
where \(Comment\) is the comment text entity. \(Text\) is the content of the comment text. \(TextTime\) is the posting time of the comment. \(TextWords\) is the set of the characteristic word entities contained in the comment. \(TextViews\) is the set of the viewpoint phrase entities contained in the comment. This study treats the viewpoint phrase as the minimal unit for semantic feature representation. Considering the syntactic structure and grammatical characteristics of tourism reviews, a BERT-BiLSTM-CRF model is employed to extract characteristic words from raw text. Subsequently, the HanLP toolkit is used to perform syntactic parsing, and characteristic words are combined based on predefined grammatical rules to construct structured viewpoint phrases. Given the short-text nature of viewpoint phrases, first, each viewpoint phrase is transformed into a distributed vector representation via Word2 Vec embedding. Then, a PyTorch (version 2.5.1)-based text classification model is applied to identify its thematic category. Finally, the SnowNLP pre-trained model is leveraged to determine the sentiment polarity of the phrase.