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 211–220 of 458 papers

Title	Date	Tasks	Status	Hype
Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation	Aug 5, 2021	Emotion ClassificationEmotion Recognition	—Unverified	0
Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition	Aug 4, 2021	ClassificationEmotion Classification	—Unverified	0
Emotion Classification of COVID-19 Chinese Microblogs based on the Emotion Category Description	Aug 1, 2021	ClassificationEmotion Classification	—Unverified	0
Distributed Representations of Emotion Categories in Emotion Space	Aug 1, 2021	Emotion Classification	—Unverified	0
Emotion Recognition under Consideration of the Emotion Component Process Model	Jul 27, 2021	Emotion ClassificationEmotion Recognition	—Unverified	0
BERT-like Pre-training for Symbolic Piano Music Classification Tasks	Jul 12, 2021	ClassificationEmotion Classification	CodeCode Available	1
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction	Jun 17, 2021	Common Sense ReasoningEmotion Cause Extraction	—Unverified	0
EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model	Jun 17, 2021	Emotional Speech SynthesisEmotion Classification	—Unverified	0
Twitter Sentiment Analysis	Jun 13, 2021	BIG-bench Machine LearningClassification	CodeCode Available	1
MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training	Jun 10, 2021	ClassificationEmotion Classification	CodeCode Available	0

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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