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 351–360 of 458 papers

Title	Date	Tasks	Status
Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content	Aug 5, 2019	BIG-bench Machine LearningEmotion Classification	—Unverified
Persian Slang Text Conversion to Formal and Deep Learning of Persian Short Texts on Social Media for Sentiment Classification	Mar 9, 2024	Emotion ClassificationSentiment Analysis	—Unverified
Unveiling Emotions from EEG: A GRU-Based Approach	Jul 20, 2023	EEGEmotion Classification	—Unverified
PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and \#hashtags	Jun 1, 2018	BIG-bench Machine LearningEmotion Classification	—Unverified
Podlab at SemEval-2019 Task 3: The Importance of Being Shallow	Jun 1, 2019	ClassificationEmotion Classification	—Unverified
AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text	Mar 24, 2025	Continual PretrainingEmotion Classification	—Unverified
Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing	May 16, 2019	Deep LearningEEG	—Unverified
UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings	Sep 1, 2017	Emotion ClassificationEnsemble Learning	—Unverified
Probabilistic Ensembles of Zero- and Few-Shot Learning Models for Emotion Classification	Sep 1, 2021	Emotion ClassificationFew-Shot Learning	—Unverified
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets	Jun 1, 2018	Emotion ClassificationGeneral 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