## 2 Methods
### Data Analysis  
#### 2.4.1 LDA-Gibbs Text Theme Model Construction  
This study conducted text mining on a preprocessed social media dataset based on the Latent Dirichlet Allocation (LDA). Natural language text was transformed into structured feature text. The LDA-Gibbs model was employed for semantic model construction and thematic analysis, including determining the number of themes and exploring their content. The LDA-Gibbs model, introduced in 2003 by [PERSON] and others, is a three-layer Bayesian probability model that is a powerful tool for analyzing the theme distribution in large textual datasets [55; 56]. The fundamental concept is to represent documents as random mixtures of latent themes, each characterized by a distribution over words [55]. It provides a probabilistic representation of the themes in each document within a document collection. Analyzing a subset of documents and extracting their theme distributions makes it possible to perform theme clustering or text classification based on these distributions. LDA is an unsupervised machine learning method, eliminating the need for labeled data during model training. It is well-suited for selecting representative samples from vast datasets, classifying them, and annotating them [57]. Currently, LDA-Gibbs is widely applied in text mining, theme analysis, and related fields due to its stable and accurate results during the modeling process [58]. High Frequency Feature Word Extraction  
In contrast to English words separated by spaces, the Chinese written language arranges characters individually without apparent gaps between words. To process this information, segmentation of the sequence of equally spaced Chinese characters is necessary for word separation [59]. Thus, a word segmentation tool is required to process the obtained text data, breaking down the original continuous text into word sequences and filtering out words with no practical meaning, i.e., stop words. This study used the Jieba segmentation tool to remove stop words from the Dianping text data. Subsequently, a word frequency analysis was performed on the text data to extract high-frequency feature words and generate a vocabulary frequency table. To avoid ambiguity, this study did not include the frequency of single characters in the statistics of high-frequency feature words. The top 100 high-frequency feature words were selected to gain initial insights into visitors' perceptions and the importance of urban river corridors. Subsequently, irrelevant entries were manually removed while concurrently retaining high-frequency terms that appeared together. The top 50 high-frequency feature words were selected to generate the semantic network relationship diagram. This diagram allowed for the analysis of node centrality, providing a more intuitive reflection of the semantic structure and interrelations among high-frequency feature words. Determining the Number of Themes  
Empirical studies confirm a direct relationship between the effectiveness of LDA theme extraction and the number of latent themes, denoted as \(K\)[60]. The results of this extraction are susceptible to the value of \(K\). Perplexity, a standard evaluation metric in natural language processing [61], which plays a crucial role in assessing the quality of models. Generally, perplexity decreases with an increasing number of themes, with smaller perplexity indicating more vital generalization ability. The \"elbow\" point, representing the optimal number of themes, exhibits a significant perplexity difference with the preceding point and a minor difference with the subsequent point [62]. This study employed the method of calculating perplexity and plotted the theme-perplexity curve to determine the optimal number of themes. In the LDA-Gibbs theme model, the formula for calculating perplexity is as follows [55]:  
\[Perplesity(D)=\exp\left\{-\frac{\sum_{d-1}^{M}\log p(w_{d})}{\sum_{d-1}^{M}N_{d} }\right\} \tag{1}\]  
where, \(D\) represents the test set in the corpus, consisting of \(M\) documents.