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 301–310 of 458 papers

Title	Date	Tasks	Status	Hype
Speaker-invariant Affective Representation Learning via Adversarial Training	Nov 4, 2019	Emotion ClassificationEmotion Recognition	—Unverified	0
Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model	Nov 1, 2019	ArticlesEmotion Classification	—Unverified	0
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge	Nov 1, 2019	Emotion ClassificationGeneral Classification	—Unverified	0
Multi-Reference Neural TTS Stylization with Adversarial Cycle Consistency	Oct 25, 2019	Emotion ClassificationStyle Transfer	—Unverified	0
Objective Human Affective Vocal Expression Detection and Automatic Classification with Stochastic Models and Learning Systems	Oct 4, 2019	ClassificationEmotion Classification	—Unverified	0
Jointly Learning to Detect Emotions and Predict Facebook Reactions	Sep 24, 2019	Emotion Classification	—Unverified	0
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion Regression	Sep 11, 2019	Deep AttentionEmotion Classification	CodeCode Available	0
Contextualized Representations for Low-resource Utterance Tagging	Sep 1, 2019	Emotion Classification	—Unverified	0
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation	Aug 30, 2019	Emotion ClassificationEmotion Recognition	CodeCode Available	1
Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning	Aug 23, 2019	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