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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 151–175 of 458 papers

TitleStatusHype
Emotion Classification of COVID-19 Chinese Microblogs based on the Emotion Category Description—0
Emotion Corpus Construction Based on Selection from Hashtags—0
Emotion Detection from EEG using Transfer Learning—0
Emotion Detection through Body Gesture and Face—0
Emotion Detection with Transformers: A Comparative Study—0
Emotion Distribution Learning from Texts—0
Automatic Emotion Experiencer Recognition—0
Emotion Intensity and its Control for Emotional Voice Conversion—0
An Interpretable Neural Network with Topical Information for Relevant Emotion Ranking—0
Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices—0
Emotion recognition by fusing time synchronous and time asynchronous representations—0
Emotion Recognition from Microblog Managing Emoticon with Text and Classifying using 1D CNN—0
Anubhuti -- An annotated dataset for emotional analysis of Bengali short stories—0
Emotion Recognition under Consideration of the Emotion Component Process Model—0
Emotions and NLP: Future Directions—0
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation—0
EmotionX-AR: CNN-DCNN autoencoder based Emotion Classifier—0
EmotionX-Area66: Predicting Emotions in Dialogues using Hierarchical Attention Network with Sequence Labeling—0
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogues—0
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogue—0
EmotionX-HSU: Adopting Pre-trained BERT for Emotion Classification—0
EmotionX-JTML: Detecting emotions with Attention—0
EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis—0
Continuing Pre-trained Model with Multiple Training Strategies for Emotional Classification—0
EMTC: Multilabel Corpus in Movie Domain for Emotion Analysis in Conversational Text—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