## 4 Results and Discussion
### Topic Analysis  
The initial step in topic analysis involved determining an appropriate number of topics. Our study conducted a comparison of perplexity scores and coherence scores for different numbers of topics. The lower the perplexity score, and the higher the coherence score, and the more reasonable the number of topics. The review of the perplexity score indicates the optimal configuration of the 2021-2023 dataset. As shown in Figure 7, the lowest perplexity score occurs on six topics, where, although the coherence score is not the highest, it is only slightly lower than the highest score. If we determine the number of topics to be the five with the highest coherence score, the perplexity score will increase significantly, indicating that it is appropriate to determine the number of topics as six (as shown in Figure 8, the intertopic distance map of topic modeling also indicates that the number of six topics is appropriate). Using the same method as above, the optimal number of topics is identified as seven for 2021, six for 2022, and  
Following the identification of the optimal number of topics for each analyzed period, topic analysis was conducted for the overall 2021-2023 timeframe as well as separately for each year. An intertropic distance map was utilized to provide a comprehensive visual overview of the topic model's structure. In this map, each bubble represents one of the topics identified by the analysis; the size of a bubble correlates with the frequency of the topic within the corpus. The number of bubbles on the map corresponds to the number of distinct topics determined to be optimal, while the spatial distance between any two bubbles indicates the degree of relevance or overlap between those topics. Larger bubbles that show minimal overlap with others are indicative of superior topic modeling effects and the enhanced interpretability of the data. As illustrated in Figure 8, the intertopic distance maps confirm that the topic clustering is effective, demonstrating clear distinctions and meaningful relationships among the topics. In our analysis, after testing various values of \(\lambda\), we ultimately selected \(\lambda=0.5\). This choice reflects a balanced preference for words that frequently appear across the dataset while also being uniquely associated with the identified topics. This setting is particularly effective for extracting keywords that succinctly and accurately capture the core essence of each topic. The topics identified through this methodological approach for the overall pe  
Figure 8: Intertopic distance map of topic modeling. riod of 2021-2023, as well as for each individual year -- 2021, 2022, and 2023 -- are concisely summarized in Table 6. Additionally, the keywords extracted for each topic, which provide insight into the thematic focus and semantic richness of the discussions, are detailed in Appendix A. The analysis of topics over the period from 2021 to 2023 reveals notable consistency and stability in the themes discussed each year, reflecting ongoing priorities and shifts in focus. The first focus is high-quality development. This theme remains central throughout the years, emphasizing the pursuit of common prosperity through economic advancement. Keywords such as technological innovation, agricultural modernization, regional coordination, industrial chain enhancement, and green development underscore China's strategies for high-quality growth. The second focus is central role of CPC leadership. The leadership of the CPC consistently emerges as a significant topic, especially pronounced during the CPC's 100 th anniversary in 2021 and the 20 th National Congress in 2022. In 2023, while the focus on specific CPC leadership events diminishes, discussions remain intertwined with themes of high-quality development. The third focus is Chinese nationalism and global contributions.