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

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Showing 276–300 of 458 papers

TitleStatusHype
University of Indonesia at SemEval-2025 Task 11: Evaluating State-of-the-Art Encoders for Multi-Label Emotion Detection—0
Unsupervised Representations Improve Supervised Learning in Speech Emotion Recognition—0
Unveiling Emotions from EEG: A GRU-Based Approach—0
Utilizing Deep Learning Towards Multi-modal Bio-sensing and Vision-based Affective Computing—0
UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings—0
Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study—0
VEMOCLAP: A video emotion classification web application—0
Versatile audio-visual learning for emotion recognition—0
VISU at WASSA 2023 Shared Task: Detecting Emotions in Reaction to News Stories Leveraging BERT and Stacked Embeddings—0
WASSA@IITK at WASSA 2021: Multi-task Learning and Transformer Finetuning for Emotion Classification and Empathy Prediction—0
x-enVENT: A Corpus of Event Descriptions with Experiencer-specific Emotion and Appraisal Annotations—0
x-vectors meet emotions: A study on dependencies between emotion and speaker recognition—0
YNU-HPCC at SemEval-2022 Task 5: Multi-Modal and Multi-label Emotion Classification Based on LXMERT—0
Fine-Grained Emotion Recognition in Olympic Tweets Based on Human Computation—0
Flood of Techniques and Drought of Theories: Emotion Mining in Disasters—0
FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis—0
Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognition—0
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations—0
Gaze-enhanced Crossmodal Embeddings for Emotion Recognition—0
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework—0
Generating a Word-Emotion Lexicon from \#Emotional Tweets—0
GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception—0
Group Visual Sentiment Analysis—0
Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation—0
HCAM -- Hierarchical Cross Attention Model for Multi-modal Emotion Recognition—0
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All datasetsCMU-MOSEISemEval 2018 Task 1E-cEWALKMFAROCStoriesArmanEmoCAER-DynamicRAVDESSShortPersianEmo

Benchmark Results

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