We report on building a computational model of romantic relationships in a corpus
of historical literary texts. We frame this task as a ranking problem in which, for
a given character, we try to assign the highest rank to the character with whom (s)he
is most likely to be romantically involved. As data we use a publicly available corpus
of French 17th and 18th century plays (http://www.theatre-classique.fr/) which is
well suited for this type of analysis because of the rich markup it provides (e.g.
indications of characters speaking). We focus on distributional, so-called second-order
features, which capture how speakers are contextually embedded in the texts. At a
mean reciprocal rate (MRR) of 0.9 and MRR@1 of 0.81, our results are encouraging,
suggesting that this approach might be successfully extended to other forms of social
interactions in literature, such as antagonism or social power relations.