The Shapes of Stories

Abstract

Sentiment analysis has gained widespread adoption in many fields, but not—until now—in literary studies. Scholars have lacked a robust methodology that adapts the tool to the skills and questions central to literary scholars. Also lacking has been quantitative data to help the scholar choose between the many models. Which model is best for which narrative, and why? By comparing over three dozen models, including the latest Deep Learning AI, the author details how to choose the correct model—or set of models—depending on the unique affective fingerprint of a narrative. The author also demonstrates how to combine a clustered close reading of textual cruxes in order to interpret a narrative. By analyzing a diverse and cross-cultural range of texts in a series of case studies, the Element highlights new insights into the many shapes of stories.

Computational Narrative Understanding: A Big Picture Analysis

“…Schmidt (2015) used topic modeling to identify thematic arcs in television screenplays, while Thompson et al (2018) used topic models to study thematic progression in philosophical texts and social media. Reagan et al (2016) used sentiment analysis to model the concept of narrative fortune (Freytag, 1895), for which Elkins (2022) provides a more in-depth study of the validity of sentiment arcs as models of narrative structure. Boyd et al (2020) used particular word types to capture three primary narrative stages, and Sap et al (2022) used the predictability of next sentences to capture the concept of narrative "flow."…”

Quantifying the retention of emotions across story retellings

“…Emotions have been shown to influence people in the creation of stories, as Rao et al 10 demonstrated—that by priming participants with different types of emotions, the stories produced by these individuals become richer. Stories show specific emotional arcs that can be measured by word-based sentiment detection 11 , 12 . Nabi and Green 13 suggest that emotional flow–how emotions change within a narrative–intensifies immersion within the narrative world (i.e., transportation).…”