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 226–250 of 458 papers

TitleStatusHype
Improved Speech Emotion Recognition using Transfer Learning and Spectrogram Augmentation—0
Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition—0
Distributed Representations of Emotion Categories in Emotion Space—0
Emotion Classification of COVID-19 Chinese Microblogs based on the Emotion Category Description—0
Emotion Recognition under Consideration of the Emotion Component Process Model—0
EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model—0
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction—0
MusicBERT: Symbolic Music Understanding with Large-Scale Pre-TrainingCode0
Musical Prosody-Driven Emotion Classification: Interpreting Vocalists Portrayal of Emotions Through Machine Learning—0
Multitask Learning for Emotionally Analyzing Sexual Abuse DisclosuresCode0
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots—0
Seq2Emo: A Sequence to Multi-Label Emotion Classification Model—0
Magnifying Subtle Facial Motions for Effective 4D Expression Recognition—0
Towards Interpretable and Transferable Speech Emotion Recognition: Latent Representation Based Analysis of Features, Methods and Corpora—0
Sentiment and Emotion Classification of Epidemic Related Bilingual data from Social Media—0
Enhancing Cognitive Models of Emotions with Representation LearningCode0
WASSA@IITK at WASSA 2021: Multi-task Learning and Transformer Finetuning for Emotion Classification and Empathy Prediction—0
AdCOFE: Advanced Contextual Feature Extraction in Conversations for emotion classification—0
Multi-Emotion Classification for Song Lyrics—0
MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?—0
Universal Joy A Data Set and Results for Classifying Emotions Across Languages—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
Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework—0
Emotion Transfer Using Vector-Valued Infinite Task LearningCode0
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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