  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.
