Emotion Dynamics in Movie Dialogues
- URL: http://arxiv.org/abs/2103.01345v1
- Date: Mon, 1 Mar 2021 23:02:16 GMT
- Title: Emotion Dynamics in Movie Dialogues
- Authors: Will E. Hipson and Saif M. Mohammad
- Abstract summary: We introduce a framework to track emotion dynamics through one's utterances.
We use this approach to trace emotional arcs of movie characters.
We analyze thousands of such character arcs to test hypotheses that inform our broader understanding of stories.
- Score: 25.289525325790414
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Emotion dynamics is a framework for measuring how an individual's emotions
change over time. It is a powerful tool for understanding how we behave and
interact with the world. In this paper, we introduce a framework to track
emotion dynamics through one's utterances. Specifically we introduce a number
of utterance emotion dynamics (UED) metrics inspired by work in Psychology. We
use this approach to trace emotional arcs of movie characters. We analyze
thousands of such character arcs to test hypotheses that inform our broader
understanding of stories. Notably, we show that there is a tendency for
characters to use increasingly more negative words and become increasingly
emotionally discordant with each other until about 90 percent of the narrative
length. UED also has applications in behavior studies, social sciences, and
public health.
Related papers
- Think out Loud: Emotion Deducing Explanation in Dialogues [57.90554323226896]
We propose a new task "Emotion Deducing Explanation in Dialogues" (EDEN)
EDEN recognizes emotion and causes in an explicitly thinking way.
It can help Large Language Models (LLMs) achieve better recognition of emotions and causes.
arXiv Detail & Related papers (2024-06-07T08:58:29Z) - Personality-affected Emotion Generation in Dialog Systems [67.40609683389947]
We propose a new task, Personality-affected Emotion Generation, to generate emotion based on the personality given to the dialog system.
We analyze the challenges in this task, i.e., (1) heterogeneously integrating personality and emotional factors and (2) extracting multi-granularity emotional information in the dialog context.
Results suggest that by adopting our method, the emotion generation performance is improved by 13% in macro-F1 and 5% in weighted-F1 from the BERT-base model.
arXiv Detail & Related papers (2024-04-03T08:48:50Z) - Emotion Granularity from Text: An Aggregate-Level Indicator of Mental
Health [27.00019048231393]
In psychology, variation in the ability of individuals to differentiate between emotion concepts is called emotion granularity.
High emotion granularity has been linked with better mental and physical health.
Low emotion granularity has been linked with maladaptive emotion regulation strategies and poor health outcomes.
arXiv Detail & Related papers (2024-03-04T18:12:10Z) - Dynamic Causal Disentanglement Model for Dialogue Emotion Detection [77.96255121683011]
We propose a Dynamic Causal Disentanglement Model based on hidden variable separation.
This model effectively decomposes the content of dialogues and investigates the temporal accumulation of emotions.
Specifically, we propose a dynamic temporal disentanglement model to infer the propagation of utterances and hidden variables.
arXiv Detail & Related papers (2023-09-13T12:58:09Z) - Utterance Emotion Dynamics in Children's Poems: Emotional Changes Across
Age [29.467916405081272]
We use a lexicon and a machine learning based approach to quantify characteristics of emotion dynamics determined from poems written by children of various ages.
We find increasing emotional variability, rise rates (i.e., emotional reactivity), and recovery rates (i.e., emotional regulation) with age.
arXiv Detail & Related papers (2023-06-08T17:38:14Z) - How you feelin'? Learning Emotions and Mental States in Movie Scenes [9.368590075496149]
We formulate emotion understanding as predicting a diverse and multi-label set of emotions at the level of a movie scene.
EmoTx is a multimodal Transformer-based architecture that ingests videos, multiple characters, and dialog utterances to make joint predictions.
arXiv Detail & Related papers (2023-04-12T06:31:14Z) - Why Do You Feel This Way? Summarizing Triggers of Emotions in Social
Media Posts [61.723046082145416]
We introduce CovidET (Emotions and their Triggers during Covid-19), a dataset of 1,900 English Reddit posts related to COVID-19.
We develop strong baselines to jointly detect emotions and summarize emotion triggers.
Our analyses show that CovidET presents new challenges in emotion-specific summarization, as well as multi-emotion detection in long social media posts.
arXiv Detail & Related papers (2022-10-22T19:10:26Z) - Speech Synthesis with Mixed Emotions [77.05097999561298]
We propose a novel formulation that measures the relative difference between the speech samples of different emotions.
We then incorporate our formulation into a sequence-to-sequence emotional text-to-speech framework.
At run-time, we control the model to produce the desired emotion mixture by manually defining an emotion attribute vector.
arXiv Detail & Related papers (2022-08-11T15:45:58Z) - Emotion Intensity and its Control for Emotional Voice Conversion [77.05097999561298]
Emotional voice conversion (EVC) seeks to convert the emotional state of an utterance while preserving the linguistic content and speaker identity.
In this paper, we aim to explicitly characterize and control the intensity of emotion.
We propose to disentangle the speaker style from linguistic content and encode the speaker style into a style embedding in a continuous space that forms the prototype of emotion embedding.
arXiv Detail & Related papers (2022-01-10T02:11:25Z) - Emotion Recognition under Consideration of the Emotion Component Process
Model [9.595357496779394]
We use the emotion component process model (CPM) by Scherer (2005) to explain emotion communication.
CPM states that emotions are a coordinated process of various subcomponents, in reaction to an event, namely the subjective feeling, the cognitive appraisal, the expression, a physiological bodily reaction, and a motivational action tendency.
We find that emotions on Twitter are predominantly expressed by event descriptions or subjective reports of the feeling, while in literature, authors prefer to describe what characters do, and leave the interpretation to the reader.
arXiv Detail & Related papers (2021-07-27T15:53:25Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.