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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 326–350 of 458 papers

TitleStatusHype
Korean Twitter Emotion Classification Using Automatically Built Emotion Lexicons and Fine-Grained Features—0
KU-MTL at SemEval-2018 Task 1: Multi-task Identification of Affect in Tweets—0
Learning Emotion-enriched Word Representations—0
Learning Emotion Indicators from Tweets: Hashtags, Hashtag Patterns, and Phrases—0
Learning Multi-level Deep Representations for Image Emotion Classification—0
Learning Representations of Affect from Speech—0
Learning to Compose Diversified Prompts for Image Emotion Classification—0
Leveraging Emotion-specific Features to Improve Transformer Performance for Emotion Classification—0
Leveraging Label Information for Multimodal Emotion Recognition—0
Leveraging Writing Systems Change for Deep Learning Based Chinese Emotion Analysis—0
Lingmotif-lex: a Wide-coverage, State-of-the-art Lexicon for Sentiment Analysis—0
Linguistic Template Extraction for Recognizing Reader-Emotion—0
Linguistic Template Extraction for Recognizing Reader-Emotion and Emotional Resonance Writing Assistance—0
Low Resource Pipeline for Spoken Language Understanding via Weak Supervision—0
LT3 at SemEval-2018 Task 1: A classifier chain to detect emotions in tweets—0
M2M-Gen: A Multimodal Framework for Automated Background Music Generation in Japanese Manga Using Large Language Models—0
Machine learning based animal emotion classification using audio signals—0
Machine Learning-based NLP for Emotion Classification on a Cholera X Dataset—0
Machine Learning For Classification Of Antithetical Emotional States—0
Toward cross-subject and cross-session generalization in EEG-based emotion recognition: Systematic review, taxonomy, and methods—0
Magnifying Subtle Facial Motions for Effective 4D Expression Recognition—0
Measure of Uncertainty in Human Emotions—0
MEmoBERT: Pre-training Model with Prompt-based Learning for Multimodal Emotion Recognition—0
Microblog Emotion Classification by Computing Similarity in Text, Time, and Space—0
MilaNLP @ WASSA: Does BERT Feel Sad When You Cry?—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