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 381–390 of 458 papers

Title	Date	Tasks	Status
Context-aware Cascade Attention-based RNN for Video Emotion Recognition	May 30, 2018	DecoderEmotion Classification	—Unverified
Modeling Naive Psychology of Characters in Simple Commonsense Stories	May 16, 2018	Emotion Classification	—Unverified
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories	May 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
Lingmotif-lex: a Wide-coverage, State-of-the-art Lexicon for Sentiment Analysis	May 1, 2018	Emotion ClassificationOpinion Mining	—Unverified
EMTC: Multilabel Corpus in Movie Domain for Emotion Analysis in Conversational Text	May 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
Sentence and Clause Level Emotion Annotation, Detection, and Classification in a Multi-Genre Corpus	May 1, 2018	Emotion ClassificationEmotion Recognition	—Unverified
I Know How You Feel: Emotion Recognition with Facial Landmarks	Apr 22, 2018	ClassificationEmotion Classification	—Unverified
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer Learning	Apr 18, 2018	Emotion ClassificationTransfer Learning	CodeCode Available
AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification	Apr 3, 2018	Emotion ClassificationGeneral Classification	—Unverified
EmoRL: Continuous Acoustic Emotion Classification using Deep Reinforcement Learning	Apr 3, 2018	ClassificationDeep Reinforcement Learning	—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