Estimating political positions of lawmakers has a long tradition in political science. We present the time varying text based ideal point model to study the political positions of lawmakers based on text data. In addition to identifying political positions, our model also provides insights into topical contents and their change over time. We use our model to analyze speeches given in the U.S. Senate between 1981 and 2017 and demonstrate how the results allow to conclude that partisanship between Republicans and Democrats increased in recent years. Further we investigate the political positions of speakers over time as well as at a specific point in time to identify speakers which are positioned at the extremes of their political party based on their speeches. The topics extracted are inspected to assess how their term compositions differ in dependence of the political position as well as how these term compositions change over time.

翻译：估计立法者的政治立场在政治科学方面有着悠久的传统。我们根据文本数据提出时间上不同的基于文本的理想点模型,以研究立法者的政治立场。除了确定政治立场外,我们的模型还提供对时事内容及其随时间变化的洞察力。我们利用我们的模型分析1981年至2017年美国参议院上发表的演讲,并展示结果如何得出共和党和民主党之间的党派关系近年来有所增加的结论。我们进一步调查演讲者的政治立场,以及特定的时间点,以根据他们的演讲确定处于其政党极端位置的演讲者。所摘录的专题将接受检查,以评估其任期构成在依赖政治立场方面有何不同,以及这些术语构成如何随时间而变化。