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 411–420 of 458 papers

Title	Date	Tasks	Status
IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations	Aug 27, 2018	Emotion ClassificationGeneral Classification	CodeCode Available
PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations	Apr 8, 2024	Emotion-Cause Pair ExtractionEmotion Classification	CodeCode Available
ArmanEmo: A Persian Dataset for Text-based Emotion Detection	Jul 24, 2022	Emotion ClassificationTransfer Learning	CodeCode Available
Impact of time and note duration tokenizations on deep learning symbolic music modeling	Oct 12, 2023	Emotion ClassificationInformation Retrieval	CodeCode Available
Improved acoustic-to-articulatory inversion using representations from pretrained self-supervised learning models	Oct 30, 2022	Emotion ClassificationSelf-Supervised Learning	CodeCode Available
A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data	Aug 17, 2022	EEGElectroencephalogram (EEG)	CodeCode Available
Cross-lingual Emotion Intensity Prediction	Apr 8, 2020	Cross-Lingual TransferEmotion Classification	CodeCode Available
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss Function	Oct 4, 2024	ClassificationContrastive Learning	CodeCode Available
Pop Music Highlighter: Marking the Emotion Keypoints	Feb 28, 2018	Emotion Classification	CodeCode Available
Practical Text Classification With Large Pre-Trained Language Models	Dec 4, 2018	ClassificationEmotion Classification	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