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

Showing 301325 of 458 papers

TitleStatusHype
HGSGNLP at IEST 2018: An Ensemble of Machine Learning and Deep Neural Architectures for Implicit Emotion Classification in Tweets0
Hierarchical Audio-Visual Information Fusion with Multi-label Joint Decoding for MER 20230
How Do I Look? Publicity Mining From Distributed Keyword Representation of Socially Infused News Articles0
How Have We Reacted To The COVID-19 Pandemic? Analyzing Changing Indian Emotions Through The Lens of Twitter0
Human Emotion Classification based on EEG Signals Using Recurrent Neural Network And KNN0
Hybrid Facial Expression Recognition (FER2013) Model for Real-Time Emotion Classification and Prediction0
Hyperparameters optimization for Deep Learning based emotion prediction for Human Robot Interaction0
IITP at IJCNLP-2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network0
I Know How You Feel: Emotion Recognition with Facial Landmarks0
Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation0
Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification0
Improving Multi-label Emotion Classification via Sentiment Classification with Dual Attention Transfer Network0
Improving Multi-label Emotion Classification by Integrating both General and Domain-specific Knowledge0
Integrating Emotion Distribution Networks and Textual Message Analysis for X User Emotional State Classification0
Interpretable Multimodal Emotion Recognition using Facial Features and Physiological Signals0
Interpretable Multi-Task PINN for Emotion Recognition and EDA Prediction0
Investigating Emotion-Color Association in Deep Neural Networks0
ISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect in Tweets0
IUCL at WASSA 2022 Shared Task: A Text-only Approach to Empathy and Emotion Detection0
Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification0
Joint Learning for Emotion Classification and Emotion Cause Detection0
Jointly Learning to Detect Emotions and Predict Facebook Reactions0
Joint Modeling of News Reader's and Comment Writer's Emotions0
Knowledge-guided Transformer for Joint Theme and Emotion Classification of Chinese Classical Poetry0
Korean-Specific Emotion Annotation Procedure Using N-Gram-Based Distant Supervision and Korean-Specific-Feature-Based Distant Supervision0
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Benchmark Results

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