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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 276–300 of 458 papers

TitleStatusHype
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks—0
More Diverse Dialogue Datasets via Diversity-Informed Data Collection—0
Contextualized Emotion Recognition in Conversation as Sequence Tagging—0
Team Neuro at SemEval-2020 Task 8: Multi-Modal Fine Grain Emotion Classification of Memes using Multitask Learning—0
A computational model implementing subjectivity with the 'Room Theory'. The case of detecting Emotion from Text—0
MuSE: a Multimodal Dataset of Stressed Emotion—0
Korean-Specific Emotion Annotation Procedure Using N-Gram-Based Distant Supervision and Korean-Specific-Feature-Based Distant Supervision—0
Semi-Automatic Construction and Refinement of an Annotated Corpus for a Deep Learning Framework for Emotion Classification—0
An Event-comment Social Media Corpus for Implicit Emotion Analysis—0
Extending Adversarial Attacks to Produce Adversarial Class Probability DistributionsCode0
Cross-lingual Emotion Intensity PredictionCode0
Appraisal Theories for Emotion Classification in Text—0
ECSP: A New Task for Emotion-Cause Span-Pair Extraction and Classification—0
x-vectors meet emotions: A study on dependencies between emotion and speaker recognition—0
Facial Affect Recognition in the Wild Using Multi-Task Learning Convolutional NetworkCode0
An adversarial learning framework for preserving users' anonymity in face-based emotion recognition—0
Hyperparameters optimization for Deep Learning based emotion prediction for Human Robot Interaction—0
Ensemble emotion recognizing with multiple modal physiological signals—0
Emotion Recognition from SpeechCode0
GoodNewsEveryone: A Corpus of News Headlines Annotated with Emotions, Semantic Roles, and Reader Perception—0
Fine-Grained Emotion Classification of Chinese Microblogs Based on Graph Convolution NetworksCode0
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural AnnotatorsCode0
Converting Sentiment Annotated Data to Emotion Annotated Data—0
Attentive Modality Hopping Mechanism for Speech Emotion RecognitionCode0
The phonetic bases of vocal expressed emotion: natural versus acted—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