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 101125 of 458 papers

TitleStatusHype
Speech Emotion Recognition Using Multi-hop Attention MechanismCode0
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural AnnotatorsCode0
Emotion Recognition from SpeechCode0
Emotion Classification in German Plays with Transformer-based Language Models Pretrained on Historical and Contemporary LanguageCode0
Emotion Transfer Using Vector-Valued Infinite Task LearningCode0
Pop Music Highlighter: Marking the Emotion KeypointsCode0
Emotion Action Detection and Emotion Inference: the Task and DatasetCode0
BYEL : Bootstrap Your Emotion LatentCode0
EmoTxt: A Toolkit for Emotion Recognition from TextCode0
An Ensemble Approach to Detect Emotions at an Essay LevelCode0
BrainT at IEST 2018: Fine-tuning Multiclass Perceptron For Implicit Emotion ClassificationCode0
EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese MetaphorsCode0
Emotion4MIDI: a Lyrics-based Emotion-Labeled Symbolic Music DatasetCode0
Dilated Context Integrated Network with Cross-Modal Consensus for Temporal Emotion Localization in VideosCode0
Towards More Accurate Prediction of Human Empathy and Emotion in Text and Multi-turn Conversations by Combining Advanced NLP, Transformers-based Networks, and Linguistic MethodologiesCode0
Automatically Classifying Emotions based on Text: A Comparative Exploration of Different Datasets0
An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs0
ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models0
Automatically augmenting an emotion dataset improves classification using audio0
Interpretable Image Emotion Recognition: A Domain Adaptation Approach Using Facial Expressions0
DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method0
Automated Feature Extraction on AsMap for Emotion Classification using EEG0
ECSP: A New Task for Emotion-Cause Span-Pair Extraction and Classification0
EEG emotion recognition using dynamical graph convolutional neural networks0
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots0
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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