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 126–150 of 458 papers

TitleStatusHype
An Emotional Comfort Framework for Improving User Satisfaction in E-Commerce Customer Service Chatbots—0
AfroXLMR-Social: Adapting Pre-trained Language Models for African Languages Social Media Text—0
DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings—0
Distributed Representations of Emotion Categories in Emotion Space—0
A Two-Stage Multimodal Emotion Recognition Model Based on Graph Contrastive Learning—0
Disney at IEST 2018: Predicting Emotions using an Ensemble—0
Discriminating Neutral and Emotional Speech using Neural Networks—0
AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification—0
An Analysis of Annotated Corpora for Emotion Classification in Text—0
ZSDEVC: Zero-Shot Diffusion-based Emotional Voice Conversion with Disentangled Mechanism—0
Attentive Cross-modal Connections for Deep Multimodal Wearable-based Emotion Recognition—0
Analysing the Greek Parliament Records with Emotion Classification—0
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets—0
A Comprehensive Analysis of Preprocessing for Word Representation Learning in Affective Tasks—0
Detection and Analysis of Emotion From Speech Signals—0
Deep Learning Neural Networks for Emotion Classification from Text: Enhanced Leaky Rectified Linear Unit Activation and Weighted Loss—0
Attention Driven Fusion for Multi-Modal Emotion Recognition—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
An adversarial learning framework for preserving users' anonymity in face-based emotion recognition—0
deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets—0
Decoding Emotions in Abstract Art: Cognitive Plausibility of CLIP in Recognizing Color-Emotion Associations—0
A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon—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
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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