The electoral programs of six German parties issued before the parliamentary elections of 2021 are analyzed using state-of-the-art computational tools for quantitative narrative, topic and sentiment analysis. We compare different methods for computing the textual similarity of the programs, Jaccard Bag similarity, Latent Semantic Analysis, doc2vec, and sBERT, the representational and computational complexity increasing from the 1st to the 4th method. A new similarity measure for entire documents derived from the Fowlkes Mallows Score is applied to kmeans clustering of sBERT transformed sentences. Using novel indices of the readability and emotion potential of texts computed via SentiArt (Jacobs, 2019), our data shed light on the similarities and differences of the programs regarding their length, main ideas, comprehensibility, likeability, and semantic complexity. Among others, they reveal that the programs of the SPD and CDU have the best chances to be comprehensible and likeable -all other things being equal-, and they raise the important issue of which similarity measure is optimal for comparing texts such as electoral programs which necessarily share a lot of words. While such analyses can not replace qualitative analyses or a deep reading of the texts, they offer predictions that can be verified in empirical studies and may serve as a motivation for changing aspects of future electoral programs potentially making them more comprehensible and/or likeable.

翻译：对2021年议会选举前公布的6个德国政党的选举方案进行分析时,采用了最新的计算工具进行定量叙述、主题和情绪分析。我们比较了计算程序文本相似性的不同方法:Jaccar Bag相似性、隐含语义分析、 doc2vec 和 sBERT, 其代表性和计算复杂性从第1至第4种方法不断提高。对Fowlkes Mallows分数得出的整份文件采用了一种新的类似措施。对SBERT变判的千米人组群应用了一个新的类似措施。使用SentiArt(Jacobs, 2019年)计算文本的可读性和情感潜力的新指数,我们的数据揭示了方案长度、主要想法、可理解性、可喜性和语义复杂性等的相似性和差异。除其他外,它们揭示了SPD和CDU的节目最有可能被理解和可类比的----所有其他事情都平等,它们提出的重要问题是比较文本的最佳方法,例如选举方案(Jacobart, 2019年),我们的数据揭示了在选举方案的深度预测中可能分享大量的经验分析。