Scaling political actors based on their individual characteristics and behavior helps profiling and grouping them as well as understanding changes in the political landscape. In this paper we introduce the Structural Text-Based Scaling (STBS) model to infer ideological positions of speakers for latent topics from text data. We expand the usual Poisson factorization specification for topic modeling of text data and use flexible shrinkage priors to induce sparsity and enhance interpretability. We also incorporate speaker-specific covariates to assess their association with ideological positions. Applying STBS to U.S. Senate speeches from Congress session 114, we identify immigration and gun violence as the most polarizing topics between the two major parties in Congress. Additionally, we find that, in discussions about abortion, the gender of the speaker significantly influences their position, with female speakers focusing more on women's health. We also see that a speaker's region of origin influences their ideological position more than their religious affiliation.

翻译：基于政治行为者的个体特征和行为对其进行缩放分析，有助于对其进行画像与分组，并理解政治格局的变化。本文提出结构文本缩放模型，旨在从文本数据中推断发言者在潜在议题上的意识形态立场。我们扩展了文本主题建模中常用的泊松分解设定，采用灵活的收缩先验以诱导稀疏性并提升可解释性。同时，我们纳入发言者特定的协变量以评估其与意识形态立场的关联。将STBS模型应用于美国国会第114届会议的参议院演讲数据，我们发现移民和枪支暴力是国会两大政党间最具两极分化性的议题。此外，在关于堕胎的讨论中，发言者的性别显著影响其立场，女性发言者更侧重于女性健康议题。我们还发现，发言者的出身地区对其意识形态立场的影响大于其宗教信仰。