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 201–225 of 458 papers

TitleStatusHype
Exploiting Narrative Context and A Priori Knowledge of Categories in Textual Emotion Classification—0
Exploratory Analysis of Social Media Prior to a Suicide Attempt—0
Group Visual Sentiment Analysis—0
Exploring Fine-Grained Emotion Detection in Tweets—0
Exploring Fine-Tuned Embeddings that Model Intensifiers for Emotion Analysis—0
Exploring Vision Language Models for Facial Attribute Recognition: Emotion, Race, Gender, and Age—0
Data Augmentation in Emotion Classification Using Generative Adversarial Networks—0
Extending RNN-T-based speech recognition systems with emotion and language classification—0
Extending the EmotiNet Knowledge Base to Improve the Automatic Detection of Implicitly Expressed Emotions from Text—0
A Systematic Evaluation of LLM Strategies for Mental Health Text Analysis: Fine-tuning vs. Prompt Engineering vs. RAG—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
Features Extraction Based on an Origami Representation of 3D Landmarks—0
deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets—0
Handling Ambiguity in Emotion: From Out-of-Domain Detection to Distribution Estimation—0
Emotion Analysis of Tweets Banning Education in Afghanistan—0
Fine-Grained Emotion Recognition in Olympic Tweets Based on Human Computation—0
Flood of Techniques and Drought of Theories: Emotion Mining in Disasters—0
FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis—0
Fusion with Hierarchical Graphs for Mulitmodal Emotion Recognition—0
GatedxLSTM: A Multimodal Affective Computing Approach for Emotion Recognition in Conversations—0
Gaze-enhanced Crossmodal Embeddings for Emotion Recognition—0
\#Emotional Tweets—0
Generating a Word-Emotion Lexicon from \#Emotional Tweets—0
Causing Emotion in Collocation:An Exploratory Data Analysis—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