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

Showing 1–10 of 458 papers

TitleStatusHype
NonverbalTTS: A Public English Corpus of Text-Aligned Nonverbal Vocalizations with Emotion Annotations for Text-to-Speech—0
MMAFFBen: A Multilingual and Multimodal Affective Analysis Benchmark for Evaluating LLMs and VLMsCode0
EmoBench-UA: A Benchmark Dataset for Emotion Detection in Ukrainian—0
What About Emotions? Guiding Fine-Grained Emotion Extraction from Mobile App ReviewsCode0
Emotion Classification In-Context in Spanish—0
University of Indonesia at SemEval-2025 Task 11: Evaluating State-of-the-Art Encoders for Multi-Label Emotion Detection—0
The Super Emotion Dataset—0
EmoGist: Efficient In-Context Learning for Visual Emotion Understanding—0
Interpretable Multi-Task PINN for Emotion Recognition and EDA Prediction—0
EmoMeta: A Multimodal Dataset for Fine-grained Emotion Classification in Chinese MetaphorsCode0
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Benchmark Results

#ModelMetricClaimedVerifiedStatus
1ERANN-0-4Top-1 Accuracy74.8—Unverified