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

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Showing 76–100 of 458 papers

TitleStatusHype
VLLMs Provide Better Context for Emotion Understanding Through Common Sense ReasoningCode1
PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in ConversationsCode0
Towards Bi-Hemispheric Emotion Mapping through EEG: A Dual-Stream Neural Network Approach—0
Music Recommendation Based on Facial Emotion Recognition—0
Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification—0
Risk prediction of pathological gambling on social media—0
SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification—0
Emotion Detection with Transformers: A Comparative Study—0
An Adaptive Cost-Sensitive Learning and Recursive Denoising Framework for Imbalanced SVM Classification—0
Persian Slang Text Conversion to Formal and Deep Learning of Persian Short Texts on Social Media for Sentiment Classification—0
Emotion Classification in Low and Moderate Resource Languages—0
Emotion Classification in Short English Texts using Deep Learning Techniques—0
GiMeFive: Towards Interpretable Facial Emotion ClassificationCode1
Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation—0
Only My Model On My Data: A Privacy Preserving Approach Protecting one Model and Deceiving Unauthorized Black-Box Models—0
Sociolinguistically Informed Interpretability: A Case Study on Hinglish Emotion Classification—0
English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts—0
TONE: A 3-Tiered ONtology for Emotion analysisCode0
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning—0
Investigating Shallow and Deep Learning Techniques for Emotion Classification in Short Persian TextsCode0
Topic Bias in Emotion Classification—0
Learning Arousal-Valence Representation from Categorical Emotion Labels of SpeechCode1
Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models—0
A Contextualized Real-Time Multimodal Emotion Recognition for Conversational Agents using Graph Convolutional Networks in Reinforcement Learning—0
FerNeXt: Facial Expression Recognition Using ConvNeXt with Channel AttentionCode0
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All datasetsCMU-MOSEISemEval 2018 Task 1E-cEWALKMFAROCStoriesArmanEmoCAER-DynamicRAVDESSShortPersianEmo

Benchmark Results

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