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 41–50 of 458 papers

Title	Date	Tasks	Status	Hype	Score
Domain-Invariant Representation Learning from EEG with Private Encoders	Jan 27, 2022	EEGElectroencephalogram (EEG)	CodeCode Available	1	5
None Class Ranking Loss for Document-Level Relation Extraction	May 1, 2022	Document-level Relation ExtractionEmotion Classification	CodeCode Available	1	5
FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel Attention	Oct 20, 2023	Deep LearningEmotion Classification	CodeCode Available	0	5
Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution Networks	Dec 5, 2019	Dependency ParsingEmotion Classification	CodeCode Available	0	5
Facial expression and attributes recognition based on multi-task learning of lightweight neural networks	Mar 31, 2021	Emotion ClassificationFace Identification	CodeCode Available	0	5
Facial Affect Recognition in the Wild Using Multi-Task Learning Convolutional Network	Feb 3, 2020	Emotion ClassificationMulti-Task Learning	CodeCode Available	0	5
Facial expression and attributes recognition based on multi-task learning of lightweight neural networks	Mar 31, 2021	Emotion ClassificationEmotion Recognition	CodeCode Available	0	5
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators	Dec 2, 2019	Dialogue Act ClassificationEmotional Dialogue Acts	CodeCode Available	0	5
Enhancing Cognitive Models of Emotions with Representation Learning	Apr 20, 2021	Emotion ClassificationRepresentation Learning	CodeCode Available	0	5
Exploiting Multiple EEG Data Domains with Adversarial Learning	Apr 16, 2022	EEGElectroencephalogram (EEG)	CodeCode Available	0	5

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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