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 401–425 of 458 papers

TitleStatusHype
AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification—0
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning—0
Automated Feature Extraction on AsMap for Emotion Classification using EEG—0
Automatically augmenting an emotion dataset improves classification using audio—0
Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets—0
AdCOFE: Advanced Contextual Feature Extraction in Conversations for emotion classification—0
Bootstrapped Learning of Emotion Hashtags \#hashtags4you—0
BrainEE at SemEval-2019 Task 3: Ensembling Linear Classifiers for Emotion Prediction—0
Speaker-invariant Affective Representation Learning via Adversarial Training—0
Where are We in Event-centric Emotion Analysis? Bridging Emotion Role Labeling and Appraisal-based Approaches—0
Spectro Temporal EEG Biomarkers For Binary Emotion Classification—0
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Causing Emotion in Collocation:An Exploratory Data Analysis—0
Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG—0
Characteristic-Specific Partial Fine-Tuning for Efficient Emotion and Speaker Adaptation in Codec Language Text-to-Speech Models—0
Classification of eye-state using EEG recordings: speed-up gains using signal epochs and mutual information measure—0
Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study—0
Cluster-based Deep Ensemble Learning for Emotion Classification in Internet Memes—0
CNN based music emotion classification—0
Combining Contrastive and Non-Contrastive Losses for Fine-Tuning Pretrained Models in Speech Analysis—0
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification—0
Comparison of Gender- and Speaker-adaptive Emotion Recognition—0
Context-aware Cascade Attention-based RNN for Video Emotion Recognition—0
Adaptive Transfer Learning for Multi-Label Emotion Classification—0
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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