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 201–210 of 458 papers

Title	Date	Tasks	Status
Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition	Mar 14, 2022	Arousal EstimationDominance Estimation	—Unverified
Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors	Mar 8, 2022	AttributeEmotion Classification	—Unverified
Towards Building an Open-Domain Dialogue System Incorporated with Internet Memes	Mar 8, 2022	Emotion ClassificationResponse Generation	—Unverified
Deep Learning Neural Networks for Emotion Classification from Text: Enhanced Leaky Rectified Linear Unit Activation and Weighted Loss	Mar 4, 2022	ClassificationEmotion Classification	—Unverified
Spectro Temporal EEG Biomarkers For Binary Emotion Classification	Feb 2, 2022	ClassificationEEG	—Unverified
A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation	Jan 30, 2022	Emotion ClassificationEmotion Recognition	—Unverified
Automated Feature Extraction on AsMap for Emotion Classification using EEG	Jan 28, 2022	Automated Feature EngineeringClassification	—Unverified
Learning to Compose Diversified Prompts for Image Emotion Classification	Jan 26, 2022	ClassificationEmotion Classification	—Unverified
A Pre-trained Audio-Visual Transformer for Emotion Recognition	Jan 23, 2022	Emotion ClassificationEmotion Recognition	—Unverified
Adaptive Transfer Learning for Multi-Label Emotion Classification	Jan 16, 2022	ClassificationEmotion 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