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 21–30 of 458 papers

Title	Date	Tasks	Status	Hype
Is Cross-Attention Preferable to Self-Attention for Multi-Modal Emotion Recognition?	Feb 18, 2022	Emotion ClassificationEmotion Recognition	CodeCode Available	1
PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion Recognition	Feb 11, 2022	Data AugmentationEEG	CodeCode Available	1
Domain-Invariant Representation Learning from EEG with Private Encoders	Jan 27, 2022	EEGElectroencephalogram (EEG)	CodeCode Available	1
Few-Shot Emotion Recognition in Conversation with Sequential Prototypical Networks	Sep 20, 2021	Emotion ClassificationEmotion Recognition	CodeCode Available	1
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text Classification	Sep 12, 2021	AllClassification	CodeCode Available	1
Speech Emotion Recognition with Multi-Task Learning	Sep 6, 2021	Emotion ClassificationEmotion Recognition	CodeCode Available	1
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language Representations	Sep 1, 2021	Emotion ClassificationLanguage Modeling	CodeCode Available	1
BERT-like Pre-training for Symbolic Piano Music Classification Tasks	Jul 12, 2021	ClassificationEmotion Classification	CodeCode Available	1
Twitter Sentiment Analysis	Jun 13, 2021	BIG-bench Machine LearningClassification	CodeCode Available	1
Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention	Jun 8, 2021	Emotion ClassificationEmotion Recognition	CodeCode Available	1

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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