Evolution of emotion semantics
- URL: http://arxiv.org/abs/2108.02887v1
- Date: Thu, 5 Aug 2021 23:46:22 GMT
- Title: Evolution of emotion semantics
- Authors: Aotao Xu, Jennifer E. Stellar, Yang Xu
- Abstract summary: We find evidence for semantic change in emotion words over the past century.
Rates of change were predicted in part by an emotion concept's prototypicality.
Prototypicality negatively correlated with historical rates of emotion semantic change obtained from text-based word embeddings.
- Score: 2.1706063563430646
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Humans possess the unique ability to communicate emotions through language.
Although concepts like anger or awe are abstract, there is a shared consensus
about what these English emotion words mean. This consensus may give the
impression that their meaning is static, but we propose this is not the case.
We cannot travel back to earlier periods to study emotion concepts directly,
but we can examine text corpora, which have partially preserved the meaning of
emotion words. Using natural language processing of historical text, we found
evidence for semantic change in emotion words over the past century and that
varying rates of change were predicted in part by an emotion concept's
prototypicality - how representative it is of the broader category of
"emotion". Prototypicality negatively correlated with historical rates of
emotion semantic change obtained from text-based word embeddings, beyond more
established variables including usage frequency in English and a second
comparison language, French. This effect for prototypicality did not
consistently extend to the semantic category of birds, suggesting its relevance
for predicting semantic change may be category-dependent. Our results suggest
emotion semantics are evolving over time, with prototypical emotion words
remaining semantically stable, while other emotion words evolve more freely.
Related papers
- Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification [37.823815777259036]
We introduce a method for categorizing emotions from text, which acknowledges and differentiates between the diversified similarities and distinctions of various emotions.
Our approach not only preserves high accuracy in emotion prediction but also significantly reduces the magnitude of errors in cases of misclassification.
arXiv Detail & Related papers (2024-04-02T10:06:30Z) - Exploiting Emotion-Semantic Correlations for Empathetic Response
Generation [18.284296904390143]
Empathetic response generation aims to generate empathetic responses by understanding the speaker's emotional feelings from the language of dialogue.
Recent methods capture emotional words in the language of communicators and construct them as static vectors to perceive nuanced emotions.
We propose a dynamical Emotion-Semantic Correlation Model (ESCM) for empathetic dialogue generation tasks.
arXiv Detail & Related papers (2024-02-27T11:50:05Z) - Experiencer-Specific Emotion and Appraisal Prediction [13.324006587838523]
Emotion classification in NLP assigns emotions to texts, such as sentences or paragraphs.
We focus on the experiencers of events, and assign an emotion (if any holds) to each of them.
Our experiencer-aware models of emotions and appraisals outperform the experiencer-agnostic baselines.
arXiv Detail & Related papers (2022-10-21T16:04:27Z) - 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) - Perspective-taking and Pragmatics for Generating Empathetic Responses
Focused on Emotion Causes [50.569762345799354]
We argue that two issues must be tackled at the same time: (i) identifying which word is the cause for the other's emotion from his or her utterance and (ii) reflecting those specific words in the response generation.
Taking inspiration from social cognition, we leverage a generative estimator to infer emotion cause words from utterances with no word-level label.
arXiv Detail & Related papers (2021-09-18T04:22:49Z) - 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) - A Circular-Structured Representation for Visual Emotion Distribution
Learning [82.89776298753661]
We propose a well-grounded circular-structured representation to utilize the prior knowledge for visual emotion distribution learning.
To be specific, we first construct an Emotion Circle to unify any emotional state within it.
On the proposed Emotion Circle, each emotion distribution is represented with an emotion vector, which is defined with three attributes.
arXiv Detail & Related papers (2021-06-23T14:53:27Z) - Modality-Transferable Emotion Embeddings for Low-Resource Multimodal
Emotion Recognition [55.44502358463217]
We propose a modality-transferable model with emotion embeddings to tackle the aforementioned issues.
Our model achieves state-of-the-art performance on most of the emotion categories.
Our model also outperforms existing baselines in the zero-shot and few-shot scenarios for unseen emotions.
arXiv Detail & Related papers (2020-09-21T06:10:39Z) - Emotion Correlation Mining Through Deep Learning Models on Natural
Language Text [3.23176099204268]
We try to fill the gap between emotion recognition and emotion correlation mining through natural language text from web news.
To mine emotion correlation from emotion recognition through text, three kinds of features and two deep neural network models are presented.
arXiv Detail & Related papers (2020-07-28T08:59:16Z) - Emotion Recognition From Gait Analyses: Current Research and Future
Directions [48.93172413752614]
gait conveys information about the walker's emotion.
The mapping between various emotions and gait patterns provides a new source for automated emotion recognition.
gait is remotely observable, more difficult to imitate, and requires less cooperation from the subject.
arXiv Detail & Related papers (2020-03-13T08:22:33Z)
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.