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 2650 of 458 papers

TitleStatusHype
MELLM: Exploring LLM-Powered Micro-Expression Understanding Enhanced by Subtle Motion PerceptionCode1
Few-Shot Emotion Recognition in Conversation with Sequential Prototypical NetworksCode1
GoEmotions: A Dataset of Fine-Grained EmotionsCode1
GiMeFive: Towards Interpretable Facial Emotion ClassificationCode1
Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment AnalysisCode1
Not All Negatives are Equal: Label-Aware Contrastive Loss for Fine-grained Text ClassificationCode1
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in ConversationCode1
Twitter Sentiment AnalysisCode1
Learning Arousal-Valence Representation from Categorical Emotion Labels of SpeechCode1
BERT-like Pre-training for Symbolic Piano Music Classification TasksCode1
Modeling Label Semantics for Predicting Emotional ReactionsCode1
CTAL: Pre-training Cross-modal Transformer for Audio-and-Language RepresentationsCode1
Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across CorporaCode1
A novel Fourier Adjacency Transformer for advanced EEG emotion recognitionCode1
DialogueRNN: An Attentive RNN for Emotion Detection in ConversationsCode1
Domain-Invariant Representation Learning from EEG with Private EncodersCode1
PARSE: Pairwise Alignment of Representations in Semi-Supervised EEG Learning for Emotion RecognitionCode1
Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution NetworksCode0
FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel AttentionCode0
Facial Affect Recognition in the Wild Using Multi-Task Learning Convolutional NetworkCode0
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural AnnotatorsCode0
Enhancing Cognitive Models of Emotions with Representation LearningCode0
Exploiting Multiple EEG Data Domains with Adversarial LearningCode0
Empathic Grounding: Explorations using Multimodal Interaction and Large Language Models with Conversational AgentsCode0
EmoTxt: A Toolkit for Emotion Recognition from TextCode0
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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