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 226250 of 458 papers

TitleStatusHype
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking0
An Explainable Fast Deep Neural Network for Emotion Recognition0
A new model for the implementation of positive and negative emotion recognition0
GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception0
An Event-comment Social Media Corpus for Implicit Emotion Analysis0
Group Visual Sentiment Analysis0
Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation0
HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition0
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
Towards Bi-Hemispheric Emotion Mapping through EEG: A Dual-Stream Neural Network Approach0
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
Towards Building an Open-Domain Dialogue System Incorporated with Internet Memes0
Towards Emotion Recognition: A Persistent Entropy Application0
Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation0
Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification0
Towards Interpretable and Transferable Speech Emotion Recognition: Latent Representation Based Analysis of Features, Methods and Corpora0
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
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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