## 2 Related Work
### Research Related to Topic Modeling and Sentiment Analysis  
The vast amount of user-generated text has brought significant attention to the extraction of critical information from large document collections within the (Natural Language Processing) NLP community. Sentiment analysis, also known as opinion mining, is an active area of research in NLP that aims to identify subjective information and determine the sentiment polarity (e.g., positive or negative) of a given text [40; 41]. In other words, sentiment analysis is the computational study of people's opinions, assisting users in gathering the information necessary for decision-making.  
Recently, sentiment analysis has been applied across various domains, including business, politics, and social media. For example, [PERSON] used the Latent Dirichlet Allocation (LDA) method to perform topic modeling on text reviews of ChatGPT in India, combining it with sentiment analysis using the Valence Aware Dictionary and Sentiment Reasoner (VADER) [42]. [PERSON] conducted a comprehensive analysis of key themes in the application of NLP in personalized learning, based on the LDA testing of top educational technology journals published between 2014 and 2023 [43]. [PERSON] proposed a LDA-based model, Foreground and Background LDA (FB-LDA), to distill foreground topics and filter out longstanding background topics [44]. In recent years, LDA has been widely applied by many scholars in various research fields, such as scientific topic discovery, source code analysis, and opinion mining [45; 46; 47]. By integrating probabilistic methods, the LDA model enhances the information obtained through traditional vector space models and captures the multitasking features of documents [5]. It is considered one of the simplest and most effective topic models for analyzing text documents [48]. Sentiment recognition, however, is an inherently complex problem, leading to substantial efforts to analyze and understand its various dimensions.  
SnowNLP is a Python-based natural language processing (NLP) library designed for Chinese text processing. One of its core features is sentiment analysis, which categorizes text into positive or negative emotions using built-in models trained on Chinese data. Due to its high adaptability to the Chinese linguistic context, SnowNLP performs outstandingly in analyzing public opinions, customer reviews, and social media content (e.g., Weibo, e-commerce reviews), as demonstrated in the following studies. [PERSON]'s study involved crawling tourist reviews from the Ctrip travel platform and collecting data on China's 5A scenic spots, fine-tuning SnowNLP's sentiment categorization thresholds, and analyzing the correlation between tourists' mood changes and their geographical distribution using spatial-temporal data [49]. In [PERSON]'s research on the Wuyi Mountain National Park project, key themes were extracted from online comments using the LDA model, and sentiment trends for each theme were analyzed using SnowNLP to reveal tourists' sentiment preferences for different landscapes [50]. SnowNLP has demonstrated its value in Chinese sentiment analysis in both academia and practice, making it particularly suitable for analyzing social media and user-generated content.