Modeling the evaluative nature of German personal name compounds

Abstract

German personal name compounds such as Villen-Spahn (’villa-Spahn’), Gold-Rosi (’gold-Rosi’) and Folter-Bush (’torture-Bush’) are a rather infrequent phenomenon in the German language. They have the structure of determinative compounds and serve as a nickname for a usually well-known person. According to Belosevic (2022), personal name compounds are mostly evaluative, i.e. they evaluate the person behind the name in a positive or negative way. Further research on an evaluation across different groups of compounds (politics, showbusiness, sports) is proposed. This work will investigate the evaluative nature of 413 German personal name compounds that mostly have the structure of noun as modifier and last name as head. The 131 corresponding full names will be considered as well, e.g. Jens Spahn would correspond to Villen-Spahn. The context data of compounds and names was collected from Twitter and the Leipzig Corpora Collection. The valence value of these context words, based on a valence database of Köper and Schulte im Walde (2016), will be used to investigate the evaluative nature of compounds in comparison to their names. Furthermore, the relation to and function of the modifier will be examined. The valence values will then be used to verify whether there are noticeable differences between the groups of compounds. Afterwards, a linear regression will be implemented to predict a ’delta’ value: the difference between name valence and compound valence. Several predictor variables such as name valence, compound valence, modifier valence, age, gender, political party and nationality will be used. The results reveal that compounds are both positively and negatively evaluative in comparison to their full name while highlighting the reason why they were created. Compound valence and modifier valence are only partially correlated due to modifiers being involved rather accidentally or interpreted ironically. Lastly, noticable differences between the groups can be observed with politicians being the most negative group regarding their valence values. Conducting the linear regression with different combinations of predictor variables shows that compound valence is a highly significant predictor. Also, other variables such as modifier valence, age or political party are able to compose models that predict the delta value very well.