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 401–410 of 458 papers

Title	Date	Tasks	Status
AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification	Apr 3, 2018	Emotion ClassificationGeneral Classification	—Unverified
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning	Jan 3, 2024	ClassificationContrastive Learning	—Unverified
Automated Feature Extraction on AsMap for Emotion Classification using EEG	Jan 28, 2022	Automated Feature EngineeringClassification	—Unverified
Automatically augmenting an emotion dataset improves classification using audio	Mar 30, 2018	ClassificationEmotion Classification	—Unverified
Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets	Feb 28, 2023	ClassificationData Augmentation	—Unverified
AdCOFE: Advanced Contextual Feature Extraction in Conversations for emotion classification	Apr 9, 2021	ClassificationEmotion Classification	—Unverified
Bootstrapped Learning of Emotion Hashtags \#hashtags4you	Jun 1, 2013	Emotion ClassificationSarcasm Detection	—Unverified
BrainEE at SemEval-2019 Task 3: Ensembling Linear Classifiers for Emotion Prediction	Jun 1, 2019	Emotion ClassificationGeneral Classification	—Unverified
Speaker-invariant Affective Representation Learning via Adversarial Training	Nov 4, 2019	Emotion ClassificationEmotion Recognition	—Unverified
Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches	Sep 5, 2023	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