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 141–150 of 458 papers

Title	Date	Tasks	Status
Deep Learning Neural Networks for Emotion Classification from Text: Enhanced Leaky Rectified Linear Unit Activation and Weighted Loss	Mar 4, 2022	ClassificationEmotion Classification	—Unverified
Attention Driven Fusion for Multi-Modal Emotion Recognition	Sep 23, 2020	Emotion ClassificationEmotion Recognition	—Unverified
DeepEmotex: Classifying Emotion in Text Messages using Deep Transfer Learning	Jun 12, 2022	Emotion ClassificationSentence	—Unverified
A transformer-based approach to video frame-level prediction in Affective Behaviour Analysis In-the-wild	Mar 16, 2023	Emotion Classification	—Unverified
An adversarial learning framework for preserving users' anonymity in face-based emotion recognition	Jan 16, 2020	Emotion ClassificationEmotion Recognition	—Unverified
deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets	Sep 1, 2017	Emotion ClassificationNatural Language Inference	—Unverified
Decoding Emotions in Abstract Art: Cognitive Plausibility of CLIP in Recognizing Color-Emotion Associations	May 10, 2024	Emotion Classification	—Unverified
A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon	Jun 1, 2014	Emotion ClassificationOpinion Mining	—Unverified
Data Augmentation in Emotion Classification Using Generative Adversarial Networks	Nov 2, 2017	ClassificationData Augmentation	—Unverified
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG	Mar 31, 2025	Emotion ClassificationPrompt Engineering	—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