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The electoral programs of six German parties issued before the parliamentaryelections of 2021 are analyzed using state-of-the-art computational tools forquantitative narrative, topic and sentiment analysis. We compare differentmethods for computing the textual similarity of the programs, Jaccard Bagsimilarity, Latent Semantic Analysis, doc2vec, and sBERT, the representationaland computational complexity increasing from the 1st to the 4th method. A newsimilarity measure for entire documents derived from the Fowlkes Mallows Scoreis applied to kmeans clustering of sBERT transformed sentences. Using novelindices of the readability and emotion potential of texts computed via SentiArt(Jacobs, 2019), our data shed light on the similarities and differences of theprograms regarding their length, main ideas, comprehensibility, likeability,and semantic complexity. Among others, they reveal that the programs of the SPDand CDU have the best chances to be comprehensible and likeable -all otherthings being equal-, and they raise the important issue of which similaritymeasure is optimal for comparing texts such as electoral programs whichnecessarily share a lot of words. While such analyses can not replacequalitative analyses or a deep reading of the texts, they offer predictionsthat can be verified in empirical studies and may serve as a motivation forchanging aspects of future electoral programs potentially making them morecomprehensible and/or likeable.

Electoral Programs of German Parties 2021: A Computational Analysis Of Their Comprehensibility and Likeability Based On SentiArt