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 251300 of 458 papers

TitleStatusHype
Temporal Analysis of Functional Brain Connectivity for EEG-based Emotion Recognition0
Temporal Multimodal Fusion for Video Emotion Classification in the Wild0
The Many Moods of Emotion0
The phonetic bases of vocal expressed emotion: natural versus acted0
The Power of Properties: Uncovering the Influential Factors in Emotion Classification0
The Super Emotion Dataset0
THU\_NGN at SemEval-2019 Task 3: Dialog Emotion Classification using Attentional LSTM-CNN0
Topic Bias in Emotion Classification0
Topological EEG Nonlinear Dynamics Analysis for Emotion Recognition0
Towards adversarial learning of speaker-invariant representation for speech emotion recognition0
Towards Bi-Hemispheric Emotion Mapping through EEG: A Dual-Stream Neural Network Approach0
Towards Building an Open-Domain Dialogue System Incorporated with Internet Memes0
Towards Emotion Recognition: A Persistent Entropy Application0
Towards Interpretable and Transferable Speech Emotion Recognition: Latent Representation Based Analysis of Features, Methods and Corpora0
Towards Practical Emotion Recognition: An Unsupervised Source-Free Approach for EEG Domain Adaptation0
Towards Transferable Speech Emotion Representation: On loss functions for cross-lingual latent representations0
Improved Digital Therapy for Developmental Pediatrics Using Domain-Specific Artificial Intelligence: Machine Learning Study0
Transformer-based Architecture for Empathy Prediction and Emotion Classification0
Transformer-based Self-supervised Multimodal Representation Learning for Wearable Emotion Recognition0
Two in One Go: Single-stage Emotion Recognition with Decoupled Subject-context Transformer0
Two-View Label Propagation to Semi-supervised Reader Emotion Classification0
Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification0
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories0
Universal Joy A Data Set and Results for Classifying Emotions Across Languages0
University of Indonesia at SemEval-2025 Task 11: Evaluating State-of-the-Art Encoders for Multi-Label Emotion Detection0
Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition0
Unveiling Emotions from EEG: A GRU-Based Approach0
Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing0
UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings0
Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study0
VEMOCLAP: A video emotion classification web application0
Versatile audio-visual learning for emotion recognition0
VISU at WASSA 2023 Shared Task: Detecting Emotions in Reaction to News Stories Leveraging BERT and Stacked Embeddings0
WASSA@IITK at WASSA 2021: Multi-task Learning and Transformer Finetuning for Emotion Classification and Empathy Prediction0
x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations0
x-vectors meet emotions: A study on dependencies between emotion and speaker recognition0
YNU-HPCC at SemEval-2022 Task 5: Multi-Modal and Multi-label Emotion Classification Based on LXMERT0
Features Extraction Based on an Origami Representation of 3D Landmarks0
Fine-Grained Emotion Recognition in Olympic Tweets Based on Human Computation0
Flood of Techniques and Drought of Theories: Emotion Mining in Disasters0
FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis0
Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognition0
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations0
Gaze-enhanced Crossmodal Embeddings for Emotion Recognition0
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework0
Generating a Word-Emotion Lexicon from \#Emotional Tweets0
GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception0
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
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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