Scaling analysis is a technique in computational political science that assigns a political actor (e.g. politician or party) a score on a predefined scale based on a (typically long) body of text (e.g. a parliamentary speech or an election manifesto). For example, political scientists have often used the left--right scale to systematically analyse political landscapes of different countries. NLP methods for automatic scaling analysis can find broad application provided they (i) are able to deal with long texts and (ii) work robustly across domains and languages. In this work, we implement and compare two approaches to automatic scaling analysis of political-party manifestos: label aggregation, a pipeline strategy relying on annotations of individual statements from the manifestos, and long-input-Transformer-based models, which compute scaling values directly from raw text. We carry out the analysis of the Comparative Manifestos Project dataset across 41 countries and 27 languages and find that the task can be efficiently solved by state-of-the-art models, with label aggregation producing the best results.

翻译：缩放分析是计算政治学中的一种技术，它基于（通常较长的）文本（如议会演讲或竞选宣言）为政治行动者（例如政治家或政党）赋予预定尺度上的分数。例如，政治学家常利用左右尺度系统分析不同国家的政治格局。用于自动缩放分析的NLP方法若满足以下条件即可广泛应用：（i）能处理长文本，（ii）跨领域和语言稳健运行。本研究实现并比较了两种自动分析政党宣言缩放的方法：标签聚合（一种依赖宣言中单个陈述标注的流水线策略）与基于长输入Transformer的模型（直接根据原始文本计算缩放值）。我们对包含41个国家和27种语言的比较宣言项目数据集进行分析，发现该任务可通过最先进模型高效解决，其中标签聚合取得了最佳结果。