We present a comprehensive computational study of the under-investigated
phenomenon of personal name compounds (PNCs) in German such as
Willkommens-Merkel ('Welcome-Merkel'). Prevalent in news, social media, and
political discourse, PNCs are hypothesized to exhibit an evaluative function
that is reflected in a more positive or negative perception as compared to the
respective personal full name (such as Angela Merkel). We model 321 PNCs and
their corresponding full names at discourse level, and show that PNCs bear an
evaluative nature that can be captured through a variety of computational
methods. Specifically, we assess through valence information whether a PNC is
more positively or negatively evaluative than the person's name, by applying
and comparing two approaches using (i) valence norms and (ii) pretrained
language models (PLMs). We further enrich our data with personal,
domain-specific, and extra-linguistic information and perform a range of
regression analyses revealing that factors including compound and modifier
valence, domain, and political party membership influence how a PNC is
evaluated.

我们对德语中个人名字复合词（PNCs）的少有研究现象进行了一个全面的计算研究，展示了它们在新闻、社交媒体和政治话语中的作用。通过使用各种计算方法，我们对 321 个 PNCs 及其对应的全名进行了建模，并表明 PNCs 具有可以通过计算方法捕捉到的评估性质。通过使用情感价值信息，我们评估了 PNCs 相对于人名的正面或负面评估，分别应用并比较了两种方法：价值规范和预训练语言模型（PLMs）。我们进一步丰富了我们的数据，包括个人、领域特定和超语言信息，并进行了一系列回归分析，揭示了诸如复合词和修饰词的正负性、领域和政党成员资格等因素如何影响 PNCs 的评估。