## 3 Methods
### Data Sources and Processing
#### 3.3.2 Data Processing  
Word Segmentation and High-Frequency Word Extraction. In contrast to English texts, which are naturally separated by spaces, Chinese texts require word segmentation prior to textual analysis. In this study, the Python 3.12-based third-party tool \"Jieba\" was selected for Chinese word segmentation, as it exhibits high speed, efficiency, and accuracy in processing large-scale textual data. Each comment was segmented into individual lexical units using Jieba. Words that were unrelated to the research topic or lacked clear relevance were removed. Subsequently, high-frequency words occurring more than five times across all comments were extracted to support further analysis. Construction of the Dictionary Framework. The dictionary framework was established based on core concepts from the Millennium Ecosystem Assessment (MEA) [1] and The Economics of Ecosystems and Biodiversity (TEEB) [2], described in Section 1. Additionally, detailed classifications from the Common International Classification of Ecosystem Services (CICES V5.1) [20] were utilized. Additionally, recent studies in the field of urban green space (UGS), such as those conducted by [PERSON] et al. [21], [PERSON] et al. [22], and [PERSON] et al. [5] were incorporated into the framework. Based on this framework, six evaluation dimensions of CES for urban parks were clearly defined (Table 1). Figure 3: Number of social media comments for Macau parks. This figure illustrates the number of social media comments associated with Macau’s parks. Color shading from light to dark represents the number of comments, with darker shades indicating higher comment frequencies. The legend provides further quantification of comment counts for each park. \begin{table}
\begin{tabular}{c c c c} \hline
**CES Category** & **Description** & **Corresponding Keywords** & **Example Pictures** \\ \hline \begin{tabular}{c} Recreation and \\ Tourism \\ \end{tabular} & \begin{tabular}{c} Leisure and recreational \\ activities undertaken in parks, \\ or entertainment services \\ obtained from park facilities. \\ \end{tabular} & \begin{tabular}{c} Walking, leisure, sports, \\ running, fitness, morning \\ exercise, chess, etc. \\ \end{tabular} \\ \hline \begin{tabular}{c} Esthetic \\ Appreciation \\ \end{tabular} & \begin{tabular}{c} Esthetic enjoyment derived \\ from viewing natural scenery \\ within parks. \\ \end{tabular} & \begin{tabular}{c} Beautiful, tranquil, attractive, \\ design, scenic beauty, charm, \\ artistic, captivating, etc. \\ \end{tabular} \\ \hline \begin{tabular}{c} Public Facilities \\ \end{tabular} & \begin{tabular}{c} Natural or built infrastructure \\ that provides leisure, \\ educational, and esthetic \\ services to the public. \\ \end{tabular} & \begin{tabular}{c} Facilities, benches, fitness \\ equipment, toilets, \\ playgrounds, rest areas, \\ pavilions, etc. \\ \end{tabular} \\ \hline \begin{tabular}{c} Education and \\ Knowledge \\ Dissemination \\ \end{tabular} & \begin{tabular}{c} Historical and cultural aspects \\ of parks provide \\ opportunities for public \\ education and knowledge \\ dissemination. \\ \end{tabular} & \begin{tabular}{c} History, memorials, \\ monuments, heritage sites, \\ exhibitions, culture, \\ statues, etc. \\ \end{tabular} \\ \hline \begin{tabular}{c} Landscape \\ Experience \\ \end{tabular} & \
\begin{tabular}{c} Comprehensive experiences \\ obtained by individuals \\ through interactions with \\ natural or artificial park \\ landscapes. \\ \end{tabular} &
\begin{tabular}{c} Scenery, landscape, pigeons, \\ ponds, trees, plants, lotus \\ flowers, etc. \\ \end{tabular} \\ \hline \end{tabular}
\end{table} 
Table 1: CES categories and corresponding keywords. Matching of Perception Vocabulary. A research team consisting of five experienced researchers was formed. The team was tasked with scientifically classifying high-frequency vocabulary according to the previously established CES framework. Statistical analysis and vocabulary screening were systematically performed.