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 351–400 of 458 papers

TitleStatusHype
An Empirical Analysis of the Role of Amplifiers, Downtoners, and Negations in Emotion Classification in Microblogs—0
IIIDYT at IEST 2018: Implicit Emotion Classification With Deep Contextualized Word RepresentationsCode0
A Multi-task Ensemble Framework for Emotion, Sentiment and Intensity Prediction—0
An Analysis of Annotated Corpora for Emotion Classification in Text—0
Learning Emotion-enriched Word Representations—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-AR: CNN-DCNN autoencoder based Emotion Classifier—0
EmotionX-JTML: Detecting emotions with Attention—0
EmotionX-DLC: Self-Attentive BiLSTM for Detecting Sequential Emotions in Dialogue—0
Multimodal Relational Tensor Network for Sentiment and Emotion Classification—0
KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets—0
Amrita\_student at SemEval-2018 Task 1: Distributed Representation of Social Media Text for Affects in Tweets—0
EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet—0
psyML at SemEval-2018 Task 1: Transfer Learning for Sentiment and Emotion Analysis—0
Mutux at SemEval-2018 Task 1: Exploring Impacts of Context Information On Emotion Detection—0
ELiRF-UPV at SemEval-2018 Tasks 1 and 3: Affect and Irony Detection in Tweets—0
ECNU at SemEval-2018 Task 1: Emotion Intensity Prediction Using Effective Features and Machine Learning Models—0
LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweets—0
ISCLAB at SemEval-2018 Task 1: UIR-Miner for Affect in Tweets—0
Inducing a Lexicon of Abusive Words – a Feature-Based ApproachCode0
PlusEmo2Vec at SemEval-2018 Task 1: Exploiting emotion knowledge from emoji and \#hashtags—0
FOI DSS at SemEval-2018 Task 1: Combining LSTM States, Embeddings, and Lexical Features for Affect Analysis—0
SemEval-2018 Task 1: Affect in Tweets—0
SINAI at SemEval-2018 Task 1: Emotion Recognition in Tweets—0
EMA at SemEval-2018 Task 1: Emotion Mining for Arabic—0
DL Team at SemEval-2018 Task 1: Tweet Affect Detection using Sentiment Lexicons and Embeddings—0
CrystalFeel at SemEval-2018 Task 1: Understanding and Detecting Emotion Intensity using Affective Lexicons—0
Tw-StAR at SemEval-2018 Task 1: Preprocessing Impact on Multi-label Emotion Classification—0
AffecThor at SemEval-2018 Task 1: A cross-linguistic approach to sentiment intensity quantification in tweets—0
Context-aware Cascade Attention-based RNN for Video Emotion Recognition—0
Modeling Naive Psychology of Characters in Simple Commonsense Stories—0
EMTC: Multilabel Corpus in Movie Domain for Emotion Analysis in Conversational Text—0
Lingmotif-lex: a Wide-coverage, State-of-the-art Lexicon for Sentiment Analysis—0
Sentence and Clause Level Emotion Annotation, Detection, and Classification in a Multi-Genre Corpus—0
Understanding Emotions: A Dataset of Tweets to Study Interactions between Affect Categories—0
I Know How You Feel: Emotion Recognition with Facial Landmarks—0
NTUA-SLP at SemEval-2018 Task 1: Predicting Affective Content in Tweets with Deep Attentive RNNs and Transfer LearningCode0
EmoRL: Continuous Acoustic Emotion Classification using Deep Reinforcement Learning—0
AttnConvnet at SemEval-2018 Task 1: Attention-based Convolutional Neural Networks for Multi-label Emotion Classification—0
Automatically augmenting an emotion dataset improves classification using audio—0
EEG emotion recognition using dynamical graph convolutional neural networks—0
Pop Music Highlighter: Marking the Emotion KeypointsCode0
Joint Binary Neural Network for Multi-label Learning with Applications to Emotion Classification—0
Transfer Learning for Improving Speech Emotion Classification AccuracyCode0
Variational Autoencoders for Learning Latent Representations of Speech Emotion: A Preliminary Study—0
A Novel Approach for Effective Learning in Low Resourced Scenarios—0
IITP at IJCNLP-2017 Task 4: Auto Analysis of Customer Feedback using CNN and GRU Network—0
Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video—0
Data Augmentation in Emotion Classification Using Generative Adversarial Networks—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