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Modeling narrative structure and dynamics with networks, sentiment analysis, and topic modeling.

Semi Min^1,2, Juyong Park^1,2,3

¹Graduate School of Culture Technology, Korea Advanced Institute of Science & Technology, Daejeon, Republic of Korea.

|December 5, 2019

Summary

Narrative analysis can be modeled using dynamic networks and text analysis. This approach reveals the complex, dynamic system underlying human communication and interaction patterns in stories.

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Area of Science:

Computational Linguistics
Network Science
Literary Analysis

Background:

Human communication relies heavily on narrative structure.
Understanding narrative formation is crucial for comprehending communication and dialogue.
Existing methods for narrative analysis are often aggregate-based.

Purpose of the Study:

To develop a network-based model for analyzing narrative structure.
To integrate computational linguistics methods for enriched narrative analysis.
To characterize story progression and character relationships dynamically.

Main Methods:

Modeling narrative as a dynamical growing network of characters and interactions.
Utilizing text analysis to identify sentiments and topics between characters.
Applying network growth patterns to understand story progression.

Main Results:

Demonstrated a novel network-based approach to narrative analysis.
Created interaction maps to explicitly characterize character relationships.
Showcased the method's application using Victor Hugo's Les Misérables.

Conclusions:

Network modeling and text analysis offer a powerful framework for narrative study.
This approach moves beyond traditional methods to capture narrative complexity.
The findings highlight the dynamic and systemic nature of human interaction in narratives.