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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 151–200 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
Extending RNN-T-based speech recognition systems with emotion and language classification—0
Emotion Analysis of Tweets Banning Education in Afghanistan—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
EMOVIE: A Mandarin Emotion Speech Dataset with a Simple Emotional Text-to-Speech Model—0
\#Emotional Tweets—0
EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet—0
Continuous Metric Learning For Transferable Speech Emotion Recognition and Embedding Across Low-resource Languages—0
Empathy and Distress Prediction using Transformer Multi-output Regression and Emotion Analysis with an Ensemble of Supervised and Zero-Shot Learning Models—0
Causing Emotion in Collocation:An Exploratory Data Analysis—0
EMTC: Multilabel Corpus in Movie Domain for Emotion Analysis in Conversational Text—0
English-Malay Word Embeddings Alignment for Cross-lingual Emotion Classification with Hierarchical Attention Network—0
English Prompts are Better for NLI-based Zero-Shot Emotion Classification than Target-Language Prompts—0
Converting Sentiment Annotated Data to Emotion Annotated Data—0
Enhanced Multimodal Representation Learning with Cross-modal KD—0
Convolutional neural networks pretrained on large face recognition datasets for emotion classification from video—0
Enhancing Emotion Prediction in News Headlines: Insights from ChatGPT and Seq2Seq Models for Free-Text Generation—0
Enhancing Emotion Recognition in Conversation through Emotional Cross-Modal Fusion and Inter-class Contrastive Learning—0
Cross-cultural Emotion Classification: the Effect of Emotional Intensity and Acoustic Features—0
Ensemble emotion recognizing with multiple modal physiological signals—0
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation—0
Estimating the Uncertainty in Emotion Class Labels with Utterance-Specific Dirichlet Priors—0
Evaluating the Capabilities of Large Language Models for Multi-label Emotion Understanding—0
Evaluating the Effectiveness of Data Augmentation for Emotion Classification in Low-Resource Settings—0
Event Based Emotion Classification for News Articles—0
Experiencer-Specific Emotion and Appraisal Prediction—0
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?—0
Experimenting with Distant Supervision for Emotion Classification—0
Exploiting Community Emotion for Microblog Event Detection—0
CAiRE_HKUST at SemEval-2019 Task 3: Hierarchical Attention for Dialogue Emotion Classification—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