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 351–375 of 458 papers

TitleStatusHype
Performance Evaluation of Supervised Machine Learning Techniques for Efficient Detection of Emotions from Online Content—0
Persian Slang Text Conversion to Formal and Deep Learning of Persian Short Texts on Social Media for Sentiment Classification—0
Unveiling Emotions from EEG: A GRU-Based Approach—0
PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and \#hashtags—0
Podlab at SemEval-2019 Task 3: The Importance of Being Shallow—0
AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text—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
Probabilistic Ensembles of Zero- and Few-Shot Learning Models for Emotion Classification—0
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets—0
psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis—0
Qieemo: Speech Is All You Need in the Emotion Recognition in Conversations—0
A Comparison of Personalized and Generalized Approaches to Emotion Recognition Using Consumer Wearable Devices: Machine Learning Study—0
Real-time Emotion and Gender Classification using Ensemble CNN—0
Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework—0
Recent Advancement of Emotion Cognition in Large Language Models—0
RELATE: Generating a linguistically inspired Knowledge Graph for fine-grained emotion classification—0
Representation learning through cross-modal conditional teacher-student training for speech emotion recognition—0
Rethinking Multimodal Sentiment Analysis: A High-Accuracy, Simplified Fusion Architecture—0
Revisiting Multi-modal Emotion Learning with Broad State Space Models and Probability-guidance Fusion—0
Risk prediction of pathological gambling on social media—0
See Your Heart: Psychological states Interpretation through Visual Creations—0
Selective Co-occurrences for Word-Emotion Association—0
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
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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