Predicting reader response in narrative

This paper sketches a theory of how readers (or users, or players, or viewers) respond to narratives. Such a theory can be useful for developing evaluation functions to allow for narrative outcomes that have maximum impact at particular times on the reader. The reader's response is viewed in terms of character evaluations, namely judgments of sympathy or antipathy for the agent involved in that outcome. Character development is computed in terms of transitions in reader evaluations for an agent over the time course of the narrative. To formally model character evaluations, we begin with a representation of the narrative fabula in terms of the events, their participant roles, and their temporal relations. This representation is implemented on a corpus of narratives with existing tools and standards. Reader evaluations are annotated on events in the fabula. Once high reliability in human character evaluations has been proven, a character evaluation tagger will be trained on these evaluations.

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