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 121–130 of 458 papers

Title	Date	Tasks	Status
Context-aware Cascade Attention-based RNN for Video Emotion Recognition	May 30, 2018	DecoderEmotion Classification	—Unverified
ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models	Jun 1, 2018	BIG-bench Machine LearningEmotion Classification	—Unverified
ECSP: A New Task for Emotion-Cause Span-Pair Extraction and Classification	Mar 7, 2020	Emotion Cause ExtractionEmotion-Cause Pair Extraction	—Unverified
EEG emotion recognition using dynamical graph convolutional neural networks	Mar 21, 2018	EEGEEG Emotion Recognition	—Unverified
Comparison of Gender- and Speaker-adaptive Emotion Recognition	May 1, 2014	AttributeEmotion Classification	—Unverified
EigenEmo: Spectral Utterance Representation Using Dynamic Mode Decomposition for Speech Emotion Classification	Aug 15, 2020	Emotion Classification	—Unverified
Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals	May 13, 2019	EEGElectroencephalogram (EEG)	—Unverified
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation	Nov 25, 2020	Domain AdaptationEmotion Classification	—Unverified
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification	Nov 20, 2020	Emotion ClassificationGeneral Classification	—Unverified
Combining Contrastive and Non-Contrastive Losses for Fine-Tuning Pretrained Models in Speech Analysis	Oct 21, 2022	Emotion 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