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 401–450 of 458 papers

TitleStatusHype
Advanced LSTM: A Study about Better Time Dependency Modeling in Emotion Recognition—0
Real-time Convolutional Neural Networks for Emotion and Gender ClassificationCode0
Temporal Multimodal Fusion for Video Emotion Classification in the Wild—0
deepCybErNet at EmoInt-2017: Deep Emotion Intensities in Tweets—0
MoodSwipe: A Soft Keyboard that Suggests MessageBased on User-Specified Emotions—0
UWat-Emote at EmoInt-2017: Emotion Intensity Detection using Affect Clues, Sentiment Polarity and Word Embeddings—0
A Question Answering Approach for Emotion Cause Extraction—0
DMGroup at EmoInt-2017: Emotion Intensity Using Ensemble Method—0
A Question Answering Approach to Emotion Cause Extraction—0
EmoTxt: A Toolkit for Emotion Recognition from TextCode0
Multimodal Classification for Analysing Social Media—0
MoodSwipe: A Soft Keyboard that Suggests Messages Based on User-Specified Emotions—0
Emotion Recognition by Body Movement Representation on the Manifold of Symmetric Positive Definite Matrices—0
CNN based music emotion classification—0
Group Visual Sentiment Analysis—0
Towards Wide Learning: Experiments in HealthcareCode0
Corpus Fusion for Emotion Classification—0
Microblog Emotion Classification by Computing Similarity in Text, Time, and Space—0
Selective Co-occurrences for Word-Emotion Association—0
Two-View Label Propagation to Semi-supervised Reader Emotion Classification—0
Learning Multi-level Deep Representations for Image Emotion Classification—0
How Do I Look? Publicity Mining From Distributed Keyword Representation of Socially Infused News Articles—0
Emotion Distribution Learning from Texts—0
Minimax Filter: Learning to Preserve Privacy from Inference AttacksCode0
Event Based Emotion Classification for News Articles—0
Exploring Fine-Grained Emotion Detection in Tweets—0
Linguistic Template Extraction for Recognizing Reader-Emotion—0
Emotions and NLP: Future Directions—0
Exploratory Analysis of Social Media Prior to a Suicide Attempt—0
Modelling Valence and Arousal in Facebook posts—0
Emotion Corpus Construction Based on Selection from Hashtags—0
EmoTweet-28: A Fine-Grained Emotion Corpus for Sentiment Analysis—0
Modelling Temporal Information Using Discrete Fourier Transform for Video Classification—0
Modelling Temporal Information Using Discrete Fourier Transform for Recognizing Emotions in User-generated Videos—0
Learning Representations of Affect from Speech—0
Korean Twitter Emotion Classification Using Automatically Built Emotion Lexicons and Fine-Grained Features—0
Ensemble of Hankel Matrices for Face Emotion Recognition—0
Linguistic Template Extraction for Recognizing Reader-Emotion and Emotional Resonance Writing Assistance—0
Sentence-level Emotion Classification with Label and Context Dependence—0
Detection and Analysis of Emotion From Speech Signals—0
Discriminating Neutral and Emotional Speech using Neural Networks—0
Learning Emotion Indicators from Tweets: Hashtags, Hashtag Patterns, and Phrases—0
Exploiting Community Emotion for Microblog Event Detection—0
Generating a Word-Emotion Lexicon from \#Emotional Tweets—0
A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon—0
Comparison of Gender- and Speaker-adaptive Emotion Recognition—0
Causing Emotion in Collocation:An Exploratory Data Analysis—0
Joint Modeling of News Reader's and Comment Writer's Emotions—0
Fine-Grained Emotion Recognition in Olympic Tweets Based on Human Computation—0
Bootstrapped Learning of Emotion Hashtags \#hashtags4you—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