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 201–225 of 458 papers

TitleStatusHype
Exploiting Narrative Context and A Priori Knowledge of Categories in Textual Emotion Classification—0
Exploratory Analysis of Social Media Prior to a Suicide Attempt—0
A Pre-trained Audio-Visual Transformer for Emotion Recognition—0
Exploring Fine-Grained Emotion Detection in Tweets—0
All rivers run into the sea: Unified Modality Brain-like Emotional Central Mechanism—0
Exploring Vision Language Models for Facial Attribute Recognition: Emotion, Race, Gender, and Age—0
AdCOFE: Advanced Contextual Feature Extraction in Conversations for emotion classification—0
Extending RNN-T-based speech recognition systems with emotion and language classification—0
EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis—0
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification—0
Facial expression and attributes recognition based on multi-task learning of lightweight neural networks—0
Facial expression and attributes recognition based on multi-task learning of lightweight neural networks—0
Features Extraction Based on an Origami Representation of 3D Landmarks—0
EmotionX-JTML: Detecting emotions with Attention—0
EmotionX-HSU: Adopting Pre-trained BERT for Emotion Classification—0
Context Unlocks Emotions: Text-based Emotion Classification Dataset Auditing with Large Language Models—0
Appraisal Theories for Emotion Classification in Text—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
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogue—0
Generating a Word-Emotion Lexicon from \#Emotional Tweets—0
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues—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