Emotion Classification
93 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
Emotion Classification in Low and Moderate Resource Languages
There are 7100+ active languages spoken around the world and building emotion classification for each language is labor intensive.
Emotion Classification in Short English Texts using Deep Learning Techniques
Detecting emotions in limited text datasets from under-resourced languages presents a formidable obstacle, demanding specialized frameworks and computational strategies.
Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation
The evidential uncertainty measure is extended to quantify the uncertainty in emotion distribution estimation.
Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models
The datasets employed are ImageNet, for image classification, Celeba-HQ dataset, for identity classification, and AffectNet, for emotion classification.
Sociolinguistically Informed Interpretability: A Case Study on Hinglish Emotion Classification
Emotion classification is a challenging task in NLP due to the inherent idiosyncratic and subjective nature of linguistic expression, especially with code-mixed data.
English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts
This is particularly of interest when we have access to a multilingual large language model, because we could request labels with English prompts even for non-English data.
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning
To address the above issues, we propose a two-stage emotion recognition model based on graph contrastive learning (TS-GCL).
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).
