用文本分析预测经济民族主义事件，提升政策判断力
Forecasting Binary Economic Events in Modern Mercantilism: Traditional methodologies coupled with PCA and K-means Quantitative Analysis of Qualitative Sentimental Data
- 将新闻语义嵌入降维后做主成分分析，识别关键预测特征
- 从768维向量中提取主导因子，准确区分保护主义等二元事件
- 适合关注地缘经济、政策预警的研究者和决策者
本文研究现代重商主义，其特征为经济民族主义上升、关键技术脱钩及地缘政治分裂，标志着对1945年后全球化范式的颠覆性转变。通过主成分分析（PCA）处理768维SBERT生成的语义嵌入，从精选新闻文章中提取正交潜在因子，用于区分与保护主义、技术主权及集团重组相关的二元事件结果。主成分载荷分析揭示了驱动分类性能的关键语义特征，提升了模型可解释性与预测准确性。该方法提供了一种可扩展的数据驱动框架，可通过高维文本分析定量追踪新兴重商主义动态。
原文摘要 · Abstract (English)
This paper examines Modern Mercantilism, characterized by rising economic nationalism, strategic technological decoupling, and geopolitical fragmentation, as a disruptive shift from the post-1945 globalization paradigm. It applies Principal Component Analysis (PCA) to 768-dimensional SBERT-generated semantic embeddings of curated news articles to extract orthogonal latent factors that discriminate binary event outcomes linked to protectionism, technological sovereignty, and bloc realignments. Analysis of principal component loadings identifies key semantic features driving classification performance, enhancing interpretability and predictive accuracy. This methodology provides a scalable, data-driven framework for quantitatively tracking emergent mercantilist dynamics through high-dimensional text analytics
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