Pattern Recognition in Narrative: Tracking Emotional Expression in Context
This work addresses the problem of analyzing emotional patterns in narratives for researchers in computational linguistics and digital humanities, though it appears incremental in applying existing geometric methods to new narrative contexts.
The researchers developed a geometric data analysis method to track emotional expression in narratives by analyzing character interactions and contextual evolution, applying it to literary works like 'Casablanca' and 'Madame Bovary' and extending it to Twitter streams with low emotional content.
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.