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 171–180 of 458 papers

Title	Date	Tasks	Status
EmotionX-HSU: Adopting Pre-trained BERT for Emotion Classification	Jul 23, 2019	ClassificationEmotion Classification	—Unverified
EmotionX-JTML: Detecting emotions with Attention	Jul 1, 2018	Emotion ClassificationRepresentation Learning	—Unverified
EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis	May 1, 2016	Emotion ClassificationGeneral Classification	—Unverified
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification	May 1, 2022	AttributeClassification	—Unverified
EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model	Jun 17, 2021	Emotional Speech SynthesisEmotion Classification	—Unverified
Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation	Jul 14, 2024	ClassificationEmotion Classification	—Unverified
EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet	Jun 1, 2018	Collaborative FilteringEmotion Classification	—Unverified
Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages	Mar 28, 2022	DenoisingEmotion Classification	—Unverified
Empathy and Distress Prediction using Transformer Multi-output Regression and Emotion Analysis with an Ensemble of Supervised and Zero-Shot Learning Models	May 1, 2022	ClassificationEmotion Classification	—Unverified
Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning	May 28, 2024	Contrastive LearningEmotion Classification	—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