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 341–350 of 458 papers

Title	Date	Tasks	Status
SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification	Oct 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis	Oct 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
DataSEARCH at IEST 2018: Multiple Word Embedding based Models for Implicit Emotion Classification of Tweets with Deep Learning	Oct 1, 2018	ClassificationEmotion Classification	CodeCode Available
Disney at IEST 2018: Predicting Emotions using an Ensemble	Oct 1, 2018	Emotion ClassificationLanguage Modeling	—Unverified
HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets	Oct 1, 2018	Common Sense ReasoningEmotion Classification	—Unverified
Joint Learning for Emotion Classification and Emotion Cause Detection	Oct 1, 2018	ClassificationEmotion Classification	—Unverified
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking	Oct 1, 2018	Common Sense ReasoningEmotion Classification	—Unverified
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network	Oct 1, 2018	ClassificationEmotion Classification	—Unverified
Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion Recognition	Sep 13, 2018	Emotion ClassificationEmotion Recognition	CodeCode Available
NTUA-SLP at IEST 2018: Ensemble of Neural Transfer Methods for Implicit Emotion Classification	Sep 3, 2018	Emotion ClassificationGeneral 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