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 151175 of 458 papers

TitleStatusHype
A Multi-task Learning Framework for Evaluating Machine Translation of Emotion-loaded User-generated Content0
Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework0
A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics0
CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons0
Crowdsourcing and Validating Event-focused Emotion Corpora for German and English0
Aspect-Based Emotion Analysis and Multimodal Coreference: A Case Study of Customer Comments on Adidas Instagram Posts0
A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction0
Cross-Task Inconsistency Based Active Learning (CTIAL) for Emotion Recognition0
Cross-modal Context Fusion and Adaptive Graph Convolutional Network for Multimodal Conversational Emotion Recognition0
A Simple Attention-Based Mechanism for Bimodal Emotion Classification0
Cross-Language Speech Emotion Recognition Using Multimodal Dual Attention Transformers0
A Robust Framework for Deep Learning Approaches to Facial Emotion Recognition and Evaluation0
Amrita\_student at SemEval-2018 Task 1: Distributed Representation of Social Media Text for Affects in Tweets0
Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition0
A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study0
A CCG-based Approach to Fine-Grained Sentiment Analysis0
Cross-cultural Emotion Classification: the Effect of Emotional Intensity and Acoustic Features0
Corpus Fusion for Emotion Classification0
Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video0
Converting Sentiment Annotated Data to Emotion Annotated Data0
A Question Answering Approach to Emotion Cause Extraction0
Controlling for Confounders in Multimodal Emotion Classification via Adversarial Learning0
Contrastive Learning of Subject-Invariant EEG Representations for Cross-Subject Emotion Recognition0
A Question Answering Approach for Emotion Cause Extraction0
Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages0
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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