Feuding Families and Former Friends: Unsupervised Learning for Dynamic Fictional Relationships

Understanding how a fictional relationship between two characters changes over time (e.g., from best friends to sworn enemies) is a key challenge in digital humanities scholarship. We present a novel unsupervised neural network for this task that incorporates dictionary learning to generate interpretable, accurate relationship trajectories. While previous work on characterizing literary relationships relies on plot summaries annotated with predefined labels, our model jointly learns a set of global relationship descriptors as well as a trajectory over these descriptors for each relationship in a dataset of raw text from novels. We find that our model learns descriptors of events (e.g., marriage or murder) as well as interpersonal states (love, sadness). Our model outperforms topic model baselines on two crowdsourced tasks, and we also find interesting correlations to annotations in an existing dataset. 1 Describing Character Relationships When two characters in a book break bread, is their meal just a result of biological needs or does it mean more? Cognard-Black et al. (2014) argue that this simple interaction reflects the diversity and background of the characters, while Foster (2009) suggests that the tone of a meal can portend either good or ill for the rest of the book. To support such theories, scholars use their literary expertise to draw connections between disparate books: Gabriel Conroy’s dissonance from his family at a sumptuous feast in Joyce’s The Dead, the frustration of Tyler’s mother in Dinner at the Homesick Restaurant, and the grudging love love sadness joy