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Related Experiment Video

Updated: Oct 19, 2025

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

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Emotion dynamics in movie dialogues.

Will E Hipson¹, Saif M Mohammad²

¹Carleton University, Ottawa, ON, Canada.

|September 20, 2021

Summary

This study introduces utterance emotion dynamics (UED) metrics to track how character emotions change in stories. Findings reveal characters often use more negative language and grow emotionally distant towards the narrative

Area of Science:

Computational Linguistics
Psychology
Narrative Analysis

Background:

Understanding human emotion dynamics is crucial for behavioral and social sciences.
Existing methods for tracking emotions over time are limited in scope.
Character emotional arcs in narratives offer insights into human interaction patterns.

Purpose of the Study:

To introduce a novel framework for measuring emotion dynamics through linguistic utterances.
To develop and apply utterance emotion dynamics (UED) metrics to analyze narrative structures.
To investigate hypotheses concerning character emotional trajectories in stories.

Main Methods:

Developed utterance emotion dynamics (UED) metrics inspired by psychological theories.
Applied UED metrics to analyze emotional arcs of thousands of movie characters.

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Last Updated: Oct 19, 2025

Exploring the Use of Isolated Expressions and Film Clips to Evaluate Emotion Recognition by People with Traumatic Brain Injury

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Published on: February 11, 2017

Post-Movie Subliminal Measurement PMSM, for Investigating Implicit Social Bias

Post-Movie Subliminal Measurement PMSM, for Investigating Implicit Social Bias

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Quantified changes in word valence and inter-character emotional synchrony over narrative progression.

Main Results:

Identified a consistent pattern of increasing negative word usage in characters.
Observed a trend of growing emotional discord among characters until approximately 90% of the narrative.
Demonstrated the utility of UED in tracing complex emotional arcs in storytelling.

Conclusions:

The UED framework provides a robust method for quantifying emotion dynamics in spoken or written text.
Character emotional arcs exhibit predictable patterns related to narrative structure and conflict.
UED metrics have broad applicability in fields such as behavioral studies, social sciences, and public health.