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 221–230 of 458 papers

Title	Date	Tasks	Status
A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics	Oct 1, 2021	ClassificationEmotion Classification	—Unverified
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition	Sep 20, 2021	Contrastive LearningEEG	—Unverified
Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognition	Sep 15, 2021	Emotion ClassificationEmotion Recognition	—Unverified
Uncovering the Limits of Text-based Emotion Detection	Sep 4, 2021	Emotion Classification	CodeCode Available
Probabilistic Ensembles of Zero- and Few-Shot Learning Models for Emotion Classification	Sep 1, 2021	Emotion ClassificationFew-Shot Learning	—Unverified
Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation	Aug 5, 2021	Emotion ClassificationEmotion Recognition	—Unverified
Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition	Aug 4, 2021	ClassificationEmotion Classification	—Unverified
Distributed Representations of Emotion Categories in Emotion Space	Aug 1, 2021	Emotion Classification	—Unverified
Emotion Classification of COVID-19 Chinese Microblogs based on the Emotion Category Description	Aug 1, 2021	ClassificationEmotion Classification	—Unverified
Emotion Recognition under Consideration of the Emotion Component Process Model	Jul 27, 2021	Emotion ClassificationEmotion Recognition	—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