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 161–170 of 458 papers

Title	Date	Tasks	Status
Emotion Recognition from Microblog Managing Emoticon with Text and Classifying using 1D CNN	Jan 8, 2023	Emotion ClassificationEmotion Recognition	—Unverified
DFME: A New Benchmark for Dynamic Facial Micro-expression Recognition	Jan 3, 2023	Emotion ClassificationMicro Expression Recognition	—Unverified
Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods	Dec 16, 2022	EEGElectroencephalogram (EEG)	—Unverified
Is Style All You Need? Dependencies Between Emotion and GST-based Speaker Recognition	Nov 15, 2022	AllEmotion Classification	CodeCode Available
Improved acoustic-to-articulatory inversion using representations from pretrained self-supervised learning models	Oct 30, 2022	Emotion ClassificationSelf-Supervised Learning	CodeCode Available
Combining Contrastive and Non-Contrastive Losses for Fine-Tuning Pretrained Models in Speech Analysis	Oct 21, 2022	Emotion Classification	—Unverified
Experiencer-Specific Emotion and Appraisal Prediction	Oct 21, 2022	Emotion ClassificationPrediction	—Unverified
Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus	Oct 12, 2022	Emotion ClassificationSentiment Analysis	CodeCode Available
Machine Learning For Classification Of Antithetical Emotional States	Sep 6, 2022	ClassificationEEG	—Unverified
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure	Aug 31, 2022	channel selectionClassification	—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