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 221–230 of 458 papers

Title	Date	Tasks	Status	Hype
Efficient Speech Emotion Recognition Using Multi-Scale CNN and Attention	Jun 8, 2021	Emotion ClassificationEmotion Recognition	CodeCode Available	1
Musical Prosody-Driven Emotion Classification: Interpreting Vocalists Portrayal of Emotions Through Machine Learning	Jun 4, 2021	BIG-bench Machine LearningEmotion Classification	—Unverified	0
Seq2Emo: A Sequence to Multi-Label Emotion Classification Model	Jun 1, 2021	ClassificationDecoder	—Unverified	0
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots	Jun 1, 2021	Answer SelectionEmotion Classification	—Unverified	0
Multitask Learning for Emotionally Analyzing Sexual Abuse Disclosures	Jun 1, 2021	ClassificationEmotion Classification	CodeCode Available	0
Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task Approach	May 9, 2021	Emotion Classificationregression	CodeCode Available	1
Magnifying Subtle Facial Motions for Effective 4D Expression Recognition	May 5, 2021	Emotion ClassificationFacial Expression Recognition	—Unverified	0
Towards Interpretable and Transferable Speech Emotion Recognition: Latent Representation Based Analysis of Features, Methods and Corpora	May 5, 2021	ClusteringDecision Making	—Unverified	0
Sentiment and Emotion Classification of Epidemic Related Bilingual data from Social Media	May 4, 2021	Emotion ClassificationGeneral Classification	—Unverified	0
WASSA@IITK at WASSA 2021: Multi-task Learning and Transformer Finetuning for Emotion Classification and Empathy Prediction	Apr 20, 2021	ClassificationEmotion Classification	—Unverified	0

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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