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 81–90 of 458 papers

Title	Date	Tasks	Status
Contextualized Representations for Low-resource Utterance Tagging	Sep 1, 2019	Emotion Classification	—Unverified
Contextualized Emotion Recognition in Conversation as Sequence Tagging	Jul 1, 2020	Emotion ClassificationEmotion Recognition	—Unverified
A Parallel Corpus of Music and Lyrics Annotated with Emotions	May 1, 2012	Emotion ClassificationEmotion Recognition	—Unverified
Adaptive Transfer Learning for Multi-Label Emotion Classification	Jan 16, 2022	ClassificationEmotion Classification	—Unverified
Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models	Nov 6, 2023	Emotion Classification	—Unverified
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification	May 1, 2022	AttributeClassification	—Unverified
Continuous Adversarial Text Representation Learning for Affective Recognition	Feb 28, 2025	Adversarial TextContrastive Learning	—Unverified
Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages	Mar 28, 2022	DenoisingEmotion Classification	—Unverified
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition	Sep 20, 2021	Contrastive LearningEEG	—Unverified
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation	Apr 17, 2024	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