Pattern Recognition in Narrative: Tracking Emotional Expression in Context

TL;DR

This paper introduces a geometric data analysis method to study emotional expression in narratives, focusing on interaction and context, applied to movies, literature, and Twitter data streams.

Contribution

It presents a novel approach for analyzing emotion in narratives through geometric data analysis, emphasizing interaction and contextual evolution.

Findings

Analyzed emotional interactions in Casablanca and Madame Bovary.

Mapped narratives in Twitter streams with low emotional expression.

Demonstrated the versatility of the method across different narrative types.

Abstract

Using geometric data analysis, our objective is the analysis of narrative, with narrative of emotion being the focus in this work. The following two principles for analysis of emotion inform our work. Firstly, emotion is revealed not as a quality in its own right but rather through interaction. We study the 2-way relationship of Ilsa and Rick in the movie Casablanca, and the 3-way relationship of Emma, Charles and Rodolphe in the novel {\em Madame Bovary}. Secondly, emotion, that is expression of states of mind of subjects, is formed and evolves within the narrative that expresses external events and (personal, social, physical) context. In addition to the analysis methodology with key aspects that are innovative, the input data used is crucial. We use, firstly, dialogue, and secondly, broad and general description that incorporates dialogue. In a follow-on study, we apply our unsupervised narrative mapping to data streams with very low emotional expression. We map the narrative of Twitter streams. Thus we demonstrate map analysis of general narratives.