Three Stage Narrative Analysis; Plot-Sentiment Breakdown, Structure Learning and Concept Detection

TL;DR

This paper introduces a framework for automated narrative analysis that combines sentiment arc analysis, character context understanding, and concept detection to enhance story comprehension and aid story selection.

Contribution

The paper presents a novel multi-stage framework integrating sentiment analysis, clustering, and concept detection for narrative understanding in movie scripts.

Findings

Sentiment arcs effectively characterize story dynamics.

Clustering groups similar narrative sentiment patterns.

Analysis aids in story selection for consumers.

Abstract

Story understanding and analysis have long been challenging areas within Natural Language Understanding. Automated narrative analysis requires deep computational semantic representations along with syntactic processing. Moreover, the large volume of narrative data demands automated semantic analysis and computational learning rather than manual analytical approaches. In this paper, we propose a framework that analyzes the sentiment arcs of movie scripts and performs extended analysis related to the context of the characters involved. The framework enables the extraction of high-level and low-level concepts conveyed through the narrative. Using dictionary-based sentiment analysis, our approach applies a custom lexicon built with the LabMTsimple storylab module. The custom lexicon is based on the Valence, Arousal, and Dominance scores from the NRC-VAD dataset. Furthermore, the framework advances the analysis by clustering similar sentiment plots using Wards hierarchical clustering technique. Experimental evaluation on a movie dataset shows that the resulting analysis is helpful to consumers and readers when selecting a narrative or story.