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 351–375 of 458 papers

TitleStatusHype
Mining Sentiment Words from Microblogs for Predicting Writer-Reader Emotion Transition—0
Modeling Naive Psychology of Characters in Simple Commonsense Stories—0
Modelling Temporal Information Using Discrete Fourier Transform for Recognizing Emotions in User-generated Videos—0
Modelling Temporal Information Using Discrete Fourier Transform for Video Classification—0
Modelling Valence and Arousal in Facebook posts—0
MoodSwipe: A Soft Keyboard that Suggests MessageBased on User-Specified Emotions—0
MoodSwipe: A Soft Keyboard that Suggests Messages Based on User-Specified Emotions—0
More Diverse Dialogue Datasets via Diversity-Informed Data Collection—0
DFME: A New Benchmark for Dynamic Facial Micro-expression Recognition—0
Multi-aspect Depression Severity Assessment via Inductive Dialogue System—0
Multi-Emotion Classification for Song Lyrics—0
Multi-Microphone Speech Emotion Recognition using the Hierarchical Token-semantic Audio Transformer Architecture—0
Multimodal Classification for Analysing Social Media—0
Multimodal Emotion Classification—0
Multimodal Relational Tensor Network for Sentiment and Emotion Classification—0
Multi-Reference Neural TTS Stylization with Adversarial Cycle Consistency—0
Multiscale Fractal Analysis on EEG Signals for Music-Induced Emotion Recognition—0
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction—0
Multi-task Voice Activated Framework using Self-supervised Learning—0
MuSE: a Multimodal Dataset of Stressed Emotion—0
Musical Prosody-Driven Emotion Classification: Interpreting Vocalists Portrayal of Emotions Through Machine Learning—0
Music Recommendation Based on Facial Emotion Recognition—0
Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection—0
NLP at IEST 2018: BiLSTM-Attention and LSTM-Attention via Soft Voting in Emotion Classification—0
NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech—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