  The development of a fictional plot is centered around characters who closely
interact with each other forming dynamic social networks. In literature
analysis, such networks have mostly been analyzed without particular relation
types or focusing on roles which the characters take with respect to each
other. We argue that an important aspect for the analysis of stories and their
development is the emotion between characters. In this paper, we combine these
aspects into a unified framework to classify emotional relationships of
fictional characters. We formalize it as a new task and describe the annotation
of a corpus, based on fan-fiction short stories. The extraction pipeline which
we propose consists of character identification (which we treat as given by an
oracle here) and the relation classification. For the latter, we provide
results using several approaches previously proposed for relation
identification with neural methods. The best result of 0.45 F1 is achieved with
a GRU with character position indicators on the task of predicting undirected
emotion relations in the associated social network graph.
