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 301–325 of 458 papers

TitleStatusHype
Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation—0
Dimensional Emotion Detection from Categorical EmotionCode0
Speaker-invariant Affective Representation Learning via Adversarial Training—0
Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model—0
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge—0
Multi-Reference Neural TTS Stylization with Adversarial Cycle Consistency—0
Objective Human Affective Vocal Expression Detection and Automatic Classification with Stochastic Models and Learning Systems—0
Jointly Learning to Detect Emotions and Predict Facebook Reactions—0
PDANet: Polarity-consistent Deep Attention Network for Fine-grained Visual Emotion RegressionCode0
Contextualized Representations for Low-resource Utterance Tagging—0
Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning—0
Context-Aware Emotion Recognition NetworksCode0
Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content—0
EmotionX-HSU: Adopting Pre-trained BERT for Emotion Classification—0
EmoBed: Strengthening Monomodal Emotion Recognition via Training with Crossmodal Emotion Embeddings—0
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations—0
BrainEE at SemEval-2019 Task 3: Ensembling Linear Classifiers for Emotion Prediction—0
CAiRE\_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—0
Podlab at SemEval-2019 Task 3: The Importance of Being Shallow—0
SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations—0
THU\_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN—0
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English—0
Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing—0
Emotion Classification in Response to Tactile Enhanced Multimedia using Frequency Domain Features of Brain Signals—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