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 51–60 of 458 papers

Title	Date	Tasks	Status	Score
Exploiting Multiple EEG Data Domains with Adversarial Learning	Apr 16, 2022	EEGElectroencephalogram (EEG)	CodeCode Available	5
Facial Affect Recognition in the Wild Using Multi-Task Learning Convolutional Network	Feb 3, 2020	Emotion ClassificationMulti-Task Learning	CodeCode Available	5
FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel Attention	Oct 20, 2023	Deep LearningEmotion Classification	CodeCode Available	5
Enhanced Cross-Dataset Electroencephalogram-based Emotion Recognition using Unsupervised Domain Adaptation	Nov 19, 2024	Domain AdaptationEEG	CodeCode Available	5
Enhancing Cognitive Models of Emotions with Representation Learning	Apr 20, 2021	Emotion ClassificationRepresentation Learning	CodeCode Available	5
EmoTxt: A Toolkit for Emotion Recognition from Text	Aug 13, 2017	Emotion ClassificationEmotion Recognition	CodeCode Available	5
IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word Representations	Aug 27, 2018	Emotion ClassificationGeneral Classification	CodeCode Available	5
Impact of time and note duration tokenizations on deep learning symbolic music modeling	Oct 12, 2023	Emotion ClassificationInformation Retrieval	CodeCode Available	5
Inducing a Lexicon of Abusive Words â a Feature-Based Approach	Jun 1, 2018	Abusive Languagedomain classification	CodeCode Available	5
Empathic Grounding: Explorations using Multimodal Interaction and Large Language Models with Conversational Agents	Jul 1, 2024	Emotional IntelligenceEmotion Classification	CodeCode Available	5

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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