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
Understanding the Role of Affect Dimensions in Detecting Emotions from Tweets: A Multi-task ApproachCode1
EMelodyGen: Emotion-Conditioned Melody Generation in ABC Notation with the Musical Feature TemplateCode1
SpanEmo: Casting Multi-label Emotion Classification as Span-predictionCode1
ProxEmo: Gait-based Emotion Learning and Multi-view Proxemic Fusion for Socially-Aware Robot NavigationCode1
None Class Ranking Loss for Document-Level Relation ExtractionCode1
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in ConversationCode1
EmoNeXt: an Adapted ConvNeXt for Facial Emotion RecognitionCode1
DialogueRNN: An Attentive RNN for Emotion Detection in ConversationsCode1
Domain-Invariant Representation Learning from EEG with Private EncodersCode1
GraphCFC: A Directed Graph Based Cross-Modal Feature Complementation Approach for Multimodal Conversational Emotion RecognitionCode1
EmoVIT: Revolutionizing Emotion Insights with Visual Instruction TuningCode1
Few-Shot Emotion Recognition in Conversation with Sequential Prototypical NetworksCode1
Evaluating Emotion Arcs Across Languages: Bridging the Global Divide in Sentiment AnalysisCode1
A novel Fourier Adjacency Transformer for advanced EEG emotion recognitionCode1
Is Cross-Attention Preferable to Self-Attention for Multi-Modal Emotion Recognition?Code1
Learning Arousal-Valence Representation from Categorical Emotion Labels of SpeechCode1
PO-EMO: Conceptualization, Annotation, and Modeling of Aesthetic Emotions in German and English PoetryCode1
An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs0
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots0
AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text0
An Analysis of Annotated Corpora for Emotion Classification in Text0
Analysing the Greek Parliament Records with Emotion Classification0
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets0
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks0
An adversarial learning framework for preserving users' anonymity in face-based emotion recognition0
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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