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
SemEval-2018 Task 1: Affect in Tweets—0
Semi-Automatic Construction and Refinement of an Annotated Corpus for a Deep Learning Framework for Emotion Classification—0
SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification—0
Sentence and Clause Level Emotion Annotation, Detection, and Classification in a Multi-Genre Corpus—0
Sentence-level Emotion Classification with Label and Context Dependence—0
Sentence-Level Propaganda Detection in News Articles with Transfer Learning and BERT-BiLSTM-Capsule Model—0
Sentiment Analysis in Twitter Social Network Centered on Cryptocurrencies Using Machine Learning—0
Sentiment and Emotion Classification of Epidemic Related Bilingual data from Social Media—0
SentiXRL: An advanced large language Model Framework for Multilingual Fine-Grained Emotion Classification in Complex Text Environment—0
Seq2Emo: A Sequence to Multi-Label Emotion Classification Model—0
Seq2Emo for Multi-label Emotion Classification Based on Latent Variable Chains Transformation—0
Shallow over Deep Neural Networks: A empirical analysis for human emotion classification using audio data—0
SHeaP: Self-Supervised Head Geometry Predictor Learned via 2D Gaussians—0
SINAI at IEST 2018: Neural Encoding of Emotional External Knowledge for Emotion Classification—0
SINAI at SemEval-2018 Task 1: Emotion Recognition in Tweets—0
SINAI at SemEval-2019 Task 3: Using affective features for emotion classification in textual conversations—0
Sociolinguistically Informed Interpretability: A Case Study on Hinglish Emotion Classification—0
Song Emotion Classification of Lyrics with Out-of-Domain Data under Label Scarcity—0
Speaker-invariant Affective Representation Learning via Adversarial Training—0
Spectro Temporal EEG Biomarkers For Binary Emotion Classification—0
ST-Gait++: Leveraging spatio-temporal convolutions for gait-based emotion recognition on videos—0
SwahBERT: Language Model of Swahili—0
SymantoResearch at SemEval-2019 Task 3: Combined Neural Models for Emotion Classification in Human-Chatbot Conversations—0
Symbolic Audio Classification via Modal Decision Tree Learning—0
Team IITP-AINLPML at WASSA 2022: Empathy Detection, Emotion Classification and Personality Detection—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