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 291–300 of 458 papers

Title	Date	Tasks	Status
Machine learning based animal emotion classification using audio signals	Mar 23, 2025	Emotion Classification	—Unverified
Machine Learning-based NLP for Emotion Classification on a Cholera X Dataset	May 8, 2024	ClassificationEmotion Classification	—Unverified
Machine Learning For Classification Of Antithetical Emotional States	Sep 6, 2022	ClassificationEEG	—Unverified
Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods	Dec 16, 2022	EEGElectroencephalogram (EEG)	—Unverified
Magnifying Subtle Facial Motions for Effective 4D Expression Recognition	May 5, 2021	Emotion ClassificationFacial Expression Recognition	—Unverified
Analysing the Greek Parliament Records with Emotion Classification	May 24, 2022	ClassificationEmotion Classification	—Unverified
Measure of Uncertainty in Human Emotions	Aug 8, 2023	Decision MakingEmotion Classification	—Unverified
An adversarial learning framework for preserving users' anonymity in face-based emotion recognition	Jan 16, 2020	Emotion ClassificationEmotion Recognition	—Unverified
MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition	Oct 27, 2021	DiversityEmotion Classification	—Unverified
Microblog Emotion Classification by Computing Similarity in Text, Time, and Space	Dec 1, 2016	Emotion ClassificationGeneral Classification	—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