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 391–400 of 458 papers

Title	Date	Tasks	Status
Transformer based neural networks for emotion recognition in conversations	May 18, 2024	Causal Language ModelingEmotion Classification	CodeCode Available
Attentive Modality Hopping Mechanism for Speech Emotion Recognition	Nov 29, 2019	Emotion ClassificationEmotion Recognition	CodeCode Available
Dimensional Emotion Detection from Categorical Emotion	Nov 6, 2019	Emotion ClassificationSentence	CodeCode Available
DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning	Oct 1, 2018	ClassificationEmotion Classification	CodeCode Available
EmoTxt: A Toolkit for Emotion Recognition from Text	Aug 13, 2017	Emotion ClassificationEmotion Recognition	CodeCode Available
Facial Affect Recognition in the Wild Using Multi-Task Learning Convolutional Network	Feb 3, 2020	Emotion ClassificationMulti-Task Learning	CodeCode Available
Extending Adversarial Attacks to Produce Adversarial Class Probability Distributions	Apr 14, 2020	Adversarial AttackEmotion Classification	CodeCode Available
Exploiting Multiple EEG Data Domains with Adversarial Learning	Apr 16, 2022	EEGElectroencephalogram (EEG)	CodeCode Available
A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classification	Jan 11, 2021	Causal Emotion EntailmentCausal Inference	CodeCode Available
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators	Dec 2, 2019	Dialogue Act ClassificationEmotional Dialogue Acts	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