Emotion Classification
93 papers with code • 10 benchmarks • 27 datasets
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).
Benchmarks
These leaderboards are used to track progress in Emotion Classification
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Libraries
Use these libraries to find Emotion Classification models and implementationsDatasets
CMU-MOSEI
ROCStories
GoEmotions
MSP-IMPROV
ISEAR
EmotionLines
EmoBank
EMOPIA
Latest papers with no code
Context-Aware Siamese Networks for Efficient Emotion Recognition in Conversation
Using metric learning through a Siamese Network architecture, we achieve 57. 71 in macro F1 score for emotion classification in conversation on DailyDialog dataset, which outperforms the related work.
Cepstral Analysis Based Artifact Detection, Recognition and Removal for Prefrontal EEG
The proposed method achieves an accuracy of 99. 62% on the artifact detection task and a 82. 79% accuracy on the 6-category eye movement classification task.
The Power of Properties: Uncovering the Influential Factors in Emotion Classification
We introduce a workflow to evaluate explicit properties and their impact.
PetKaz at SemEval-2024 Task 3: Advancing Emotion Classification with an LLM for Emotion-Cause Pair Extraction in Conversations
In this paper, we present our submission to the SemEval-2023 Task~3 "The Competition of Multimodal Emotion Cause Analysis in Conversations", focusing on extracting emotion-cause pairs from dialogs.
Music Recommendation Based on Facial Emotion Recognition
Introduction: Music provides an incredible avenue for individuals to express their thoughts and emotions, while also serving as a delightful mode of entertainment for enthusiasts and music lovers.
Improved Text Emotion Prediction Using Combined Valence and Arousal Ordinal Classification
We thus redefine the emotion labeling problem by shifting it from a traditional classification model to an ordinal classification one, where discrete emotions are arranged in a sequential order according to their valence levels.
Risk prediction of pathological gambling on social media
The results of the experiments conclude that the incorporation of a time decay layer (TD) and passing the emotion classification layer (EmoBERTa) through LSTM improves the performance significantly.
SensoryT5: Infusing Sensorimotor Norms into T5 for Enhanced Fine-grained Emotion Classification
In traditional research approaches, sensory perception and emotion classification have traditionally been considered separate domains.
Emotion Detection with Transformers: A Comparative Study
In this study, we explore the application of transformer-based models for emotion classification on text data.
A Generalized Framework with Adaptive Weighted Soft-Margin for Imbalanced SVM Classification
In this paper, we present a new generalized framework with Adaptive Weight function for soft-margin Weighted SVM (AW-WSVM), which aims to enhance the issue of imbalance and outlier sensitivity in standard support vector machine (SVM) for classifying two-class data.
