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 251–275 of 458 papers

TitleStatusHype
Team Neuro at SemEval-2020 Task 8: Multi-Modal Fine Grain Emotion Classification of Memes using Multitask Learning—0
Temporal Analysis of Functional Brain Connectivity for EEG-based Emotion Recognition—0
Temporal Multimodal Fusion for Video Emotion Classification in the Wild—0
The Many Moods of Emotion—0
The phonetic bases of vocal expressed emotion: natural versus acted—0
The Power of Properties: Uncovering the Influential Factors in Emotion Classification—0
The Super Emotion Dataset—0
THU\_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN—0
Topic Bias in Emotion Classification—0
Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition—0
Towards adversarial learning of speaker-invariant representation for speech emotion recognition—0
Towards Bi-Hemispheric Emotion Mapping through EEG: A Dual-Stream Neural Network Approach—0
Towards Building an Open-Domain Dialogue System Incorporated with Internet Memes—0
Towards Emotion Recognition: A Persistent Entropy Application—0
Towards Interpretable and Transferable Speech Emotion Recognition: Latent Representation Based Analysis of Features, Methods and Corpora—0
Towards Practical Emotion Recognition: An Unsupervised Source-Free Approach for EEG Domain Adaptation—0
Towards Transferable Speech Emotion Representation: On loss functions for cross-lingual latent representations—0
Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study—0
Transformer-based Architecture for Empathy Prediction and Emotion Classification—0
Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition—0
Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer—0
Two-View Label Propagation to Semi-supervised Reader Emotion Classification—0
Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification—0
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories—0
Universal Joy A Data Set and Results for Classifying Emotions Across Languages—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