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 101–125 of 458 papers

TitleStatusHype
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation—0
A Study on Using Transfer Learning to Improve BERT Model for Emotional Classification of Chinese Lyrics—0
Data Augmentation in Emotion Classification Using Generative Adversarial Networks—0
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG—0
Decoding Emotions in Abstract Art: Cognitive Plausibility of CLIP in Recognizing Color-Emotion Associations—0
deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets—0
DeepEmotex: Classifying Emotion in Text Messages using Deep Transfer Learning—0
A transformer-based approach to video frame-level prediction in Affective Behaviour Analysis In-the-wild—0
Deep Learning Neural Networks for Emotion Classification from Text: Enhanced Leaky Rectified Linear Unit Activation and Weighted Loss—0
Detection and Analysis of Emotion From Speech Signals—0
Anubhuti -- An annotated dataset for emotional analysis of Bengali short stories—0
A Hybrid End-to-End Spatio-Temporal Attention Neural Network with Graph-Smooth Signals for EEG Emotion Recognition—0
ZSDEVC: Zero-Shot Diffusion-based Emotional Voice Conversion with Disentangled Mechanism—0
Analysing the Greek Parliament Records with Emotion Classification—0
Discriminating Neutral and Emotional Speech using Neural Networks—0
Disney at IEST 2018: Predicting Emotions using an Ensemble—0
Distributed Representations of Emotion Categories in Emotion Space—0
DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings—0
DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method—0
Interpretable Image Emotion Recognition: A Domain Adaptation Approach Using Facial Expressions—0
Context-aware Cascade Attention-based RNN for Video Emotion Recognition—0
ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models—0
ECSP: A New Task for Emotion-Cause Span-Pair Extraction and Classification—0
EEG emotion recognition using dynamical graph convolutional neural networks—0
Comparison of Gender- and Speaker-adaptive 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