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 311–320 of 458 papers

Title	Date	Tasks	Status
Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification	Apr 2, 2024	ClassificationEmotion Classification	—Unverified
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network	Oct 1, 2018	ClassificationEmotion Classification	—Unverified
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge	Nov 1, 2019	Emotion ClassificationGeneral Classification	—Unverified
Integrating Emotion Distribution Networks and Textual Message Analysis for X User Emotional State Classification	Apr 11, 2025	Emotion ClassificationSentiment Analysis	—Unverified
Interpretable Multimodal Emotion Recognition using Facial Features and Physiological Signals	Jun 5, 2023	Emotion ClassificationEmotion Recognition	—Unverified
Interpretable Multi-Task PINN for Emotion Recognition and EDA Prediction	May 14, 2025	Emotion ClassificationEmotion Recognition	—Unverified
Investigating Emotion-Color Association in Deep Neural Networks	Oct 9, 2020	Emotion Classification	—Unverified
ISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect in Tweets	Jun 1, 2018	ClassificationEmotion Classification	—Unverified
IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection	May 1, 2022	ClassificationEmotion Classification	—Unverified
Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification	Feb 3, 2018	ClassificationComputational Efficiency	—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