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 241–250 of 458 papers

Title	Date	Tasks	Status
Enhancing Cognitive Models of Emotions with Representation Learning	Apr 20, 2021	Emotion ClassificationRepresentation Learning	CodeCode Available
WASSA@IITK at WASSA 2021: Multi-task Learning and Transformer Finetuning for Emotion Classification and Empathy Prediction	Apr 20, 2021	ClassificationEmotion Classification	—Unverified
AdCOFE: Advanced Contextual Feature Extraction in Conversations for emotion classification	Apr 9, 2021	ClassificationEmotion Classification	—Unverified
Multi-Emotion Classification for Song Lyrics	Apr 1, 2021	ClassificationEmotion Classification	—Unverified
MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?	Apr 1, 2021	Emotion ClassificationMulti-Task Learning	—Unverified
Universal Joy A Data Set and Results for Classifying Emotions Across Languages	Apr 1, 2021	DiversityEmotion Classification	—Unverified
Facial expression and attributes recognition based on multi-task learning of lightweight neural networks	Mar 31, 2021	Emotion ClassificationEmotion Recognition	—Unverified
Facial expression and attributes recognition based on multi-task learning of lightweight neural networks	Mar 31, 2021	Emotion ClassificationFace Identification	—Unverified
Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework	Mar 29, 2021	Action Unit DetectionEmotion Classification	—Unverified
Emotion Transfer Using Vector-Valued Infinite Task Learning	Feb 9, 2021	BIG-bench Machine LearningEmotion Classification	CodeCode Available

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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