Emotion Classification

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).

Papers

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Showing 121–130 of 458 papers

Title	Date	Tasks	Status
Interpretable Image Emotion Recognition: A Domain Adaptation Approach Using Facial Expressions	Nov 17, 2020	Domain AdaptationEmotion Classification	—Unverified
DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method	Sep 1, 2017	Emotion ClassificationEmotion Recognition	—Unverified
ECSP: A New Task for Emotion-Cause Span-Pair Extraction and Classification	Mar 7, 2020	Emotion Cause ExtractionEmotion-Cause Pair Extraction	—Unverified
EEG emotion recognition using dynamical graph convolutional neural networks	Mar 21, 2018	EEGEEG Emotion Recognition	—Unverified
Automated Feature Extraction on AsMap for Emotion Classification using EEG	Jan 28, 2022	Automated Feature EngineeringClassification	—Unverified
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots	Jun 1, 2021	Answer SelectionEmotion Classification	—Unverified
AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text	Mar 24, 2025	Continual PretrainingEmotion Classification	—Unverified
DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings	Jun 1, 2018	Emotion ClassificationSentence	—Unverified
Distributed Representations of Emotion Categories in Emotion Space	Aug 1, 2021	Emotion Classification	—Unverified
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning	Jan 3, 2024	ClassificationContrastive Learning	—Unverified

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All datasets CMU-MOSEI SemEval 2018 Task 1E-c EWALK MFA ROCStories ArmanEmo CAER-Dynamic RAVDESS ShortPersianEmo

Benchmark Results

#	Model	Metric	Claimed	Verified	Status
1	MARLIN (ViT-L)	Accuracy	80.63	—	Unverified
2	MARLIN (ViT-B)	Accuracy	80.6	—	Unverified
3	MARLIN (ViT-S)	Accuracy	80.38	—	Unverified
4	ConCluGen	Accuracy	66.48	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	SpanEmo	Accuracy	0.6	—	Unverified
2	BERT+DK	Accuracy	0.59	—	Unverified
3	BERT-GCN	Accuracy	0.59	—	Unverified
4	Transformer (finetune)	Macro-F1	0.56	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ProxEmo (ours)	Accuracy	82.4	—	Unverified
2	STEP [bhattacharya2019step]	Accuracy	78.24	—	Unverified
3	Baseline (Vanilla LSTM) [Ewalk]	Accuracy	55.47	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	MLKNN	F-F1 score (Comb.)	0.34	—	Unverified
2	CC - XGB	F-F1 score (Comb.)	0.33	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Semi-supervision	F1	65.88	—	Unverified
2	NPN + Explanation Training	F1	30.29	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Deep ParsBERT	Macro F1	0.65	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	CAERNet	Accuracy	77.04	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	ERANN-0-4	Top-1 Accuracy	74.8	—	Unverified

#	Model	Metric	Claimed	Verified	Status
1	Deep ParsBERT	Macro F1	0.71	—	Unverified