SOTAVerified

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

Showing 5175 of 458 papers

TitleStatusHype
Investigating Shallow and Deep Learning Techniques for Emotion Classification in Short Persian TextsCode0
Improved acoustic-to-articulatory inversion using representations from pretrained self-supervised learning modelsCode0
Impact of time and note duration tokenizations on deep learning symbolic music modelingCode0
Investigating Emotion-Color Association in Deep Neural NetworksCode0
Improving Arabic Multi-Label Emotion Classification using Stacked Embeddings and Hybrid Loss FunctionCode0
Attentive Modality Hopping Mechanism for Speech Emotion RecognitionCode0
Investigation of Multimodal Features, Classifiers and Fusion Methods for Emotion RecognitionCode0
Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution NetworksCode0
IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word RepresentationsCode0
Multimodal Speech Emotion Recognition Using Audio and TextCode0
ArmanEmo: A Persian Dataset for Text-based Emotion DetectionCode0
A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero ShotCode0
A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG DataCode0
Facial Affect Recognition in the Wild Using Multi-Task Learning Convolutional NetworkCode0
Exploiting Multiple EEG Data Domains with Adversarial LearningCode0
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural AnnotatorsCode0
Extending Adversarial Attacks to Produce Adversarial Class Probability DistributionsCode0
Facial expression and attributes recognition based on multi-task learning of lightweight neural networksCode0
AIMA at SemEval-2024 Task 3: Simple Yet Powerful Emotion Cause Pair AnalysisCode0
Enhanced Cross-Dataset Electroencephalogram-based Emotion Recognition using Unsupervised Domain AdaptationCode0
A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classificationCode0
Empathic Grounding: Explorations using Multimodal Interaction and Large Language Models with Conversational AgentsCode0
Enhancing Cognitive Models of Emotions with Representation LearningCode0
Facial expression and attributes recognition based on multi-task learning of lightweight neural networksCode0
Emotion Recognition from SpeechCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1MARLIN (ViT-L)Accuracy80.63Unverified
2MARLIN (ViT-B)Accuracy80.6Unverified
3MARLIN (ViT-S)Accuracy80.38Unverified
4ConCluGenAccuracy66.48Unverified
#ModelMetricClaimedVerifiedStatus
1SpanEmoAccuracy0.6Unverified
2BERT+DKAccuracy0.59Unverified
3BERT-GCNAccuracy0.59Unverified
4Transformer (finetune)Macro-F10.56Unverified
#ModelMetricClaimedVerifiedStatus
1ProxEmo (ours)Accuracy82.4Unverified
2STEP [bhattacharya2019step]Accuracy78.24Unverified
3Baseline (Vanilla LSTM) [Ewalk]Accuracy55.47Unverified
#ModelMetricClaimedVerifiedStatus
1MLKNNF-F1 score (Comb.)0.34Unverified
2CC - XGBF-F1 score (Comb.)0.33Unverified
#ModelMetricClaimedVerifiedStatus
1Semi-supervisionF165.88Unverified
2NPN + Explanation TrainingF130.29Unverified
#ModelMetricClaimedVerifiedStatus
1Deep ParsBERTMacro F10.65Unverified
#ModelMetricClaimedVerifiedStatus
1CAERNetAccuracy77.04Unverified
#ModelMetricClaimedVerifiedStatus
1ERANN-0-4Top-1 Accuracy74.8Unverified
#ModelMetricClaimedVerifiedStatus
1Deep ParsBERTMacro F10.71Unverified