Emotion Classification
94 papers with code • 10 benchmarks • 27 datasets
Emotion classification, or emotion categorization, is the task of recognising emotions to classify them into the corresponding category. Given an input, classify it as 'neutral or no emotion' or as one, or more, of several given emotions that best represent the mental state of the subject's facial expression, words, and so on. Some example benchmarks include ROCStories, Many Faces of Anger (MFA), and GoEmotions. Models can be evaluated using metrics such as the Concordance Correlation Coefficient (CCC) and the Mean Squared Error (MSE).
Benchmarks
These leaderboards are used to track progress in Emotion Classification
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Libraries
Use these libraries to find Emotion Classification models and implementationsDatasets
CMU-MOSEI
ROCStories
GoEmotions
MSP-IMPROV
ISEAR
EmotionLines
EmoBank
EMOPIA
Latest papers with no code
Topic Bias in Emotion Classification
when funeral events are over-represented for instances labeled with sadness, despite the emotion of pride being more appropriate here.
Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models
In this work, we propose a formal definition of textual context to motivate a prompting strategy to enhance such contextual information.
A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning
In this work, we present a novel paradigm for contextualized Emotion Recognition using Graph Convolutional Network with Reinforcement Learning (conER-GRL).
WikiMT++ Dataset Card
WikiMT++ is an expanded and refined version of WikiMusicText (WikiMT), featuring 1010 curated lead sheets in ABC notation.
Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition
Speech Emotion Recognition (SER) plays a pivotal role in enhancing human-computer interaction by enabling a deeper understanding of emotional states across a wide range of applications, contributing to more empathetic and effective communication.
Hierarchical Audio-Visual Information Fusion with Multi-label Joint Decoding for MER 2023
Three different structures based on attention-guided feature gathering (AFG) are designed for deep feature fusion.
Leveraging Label Information for Multimodal Emotion Recognition
Finally, we devise a novel label-guided attentive fusion module to fuse the label-aware text and speech representations for emotion classification.
Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches
(1) Emotions are events; and this perspective is the fundament in natural language processing for emotion role labeling.
A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study
Objective: We aim to study the differences between personalized and generalized machine learning models for three-class emotion classification (neutral, stress, and amusement) using wearable biosignal data.
Measure of Uncertainty in Human Emotions
Many research explore how well computers are able to examine emotions displayed by humans and use that data to perform different tasks.
