Emotion Classification
92 papers with code • 10 benchmarks • 26 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).
Libraries
Use these libraries to find Emotion Classification models and implementationsDatasets
Most implemented papers
EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators
These neural models annotate the emotion corpora with dialogue act labels, and an ensemble annotator extracts the final dialogue act label.
Emotion Recognition from Speech
In this work, we conduct an extensive comparison of various approaches to speech based emotion recognition systems.
Cross-lingual Emotion Intensity Prediction
Consequently, we explore cross-lingual transfer approaches for fine-grained emotion detection in Spanish and Catalan tweets.
SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics
We propose SentiBERT, a variant of BERT that effectively captures compositional sentiment semantics.
Facial expression and attributes recognition based on multi-task learning of lightweight neural networks
Moreover, it is shown that the usage of our neural network as a feature extractor of facial regions in video frames and concatenation of several statistical functions (mean, max, etc.)
Facial expression and attributes recognition based on multi-task learning of lightweight neural networks
In this paper, the multi-task learning of lightweight convolutional neural networks is studied for face identification and classification of facial attributes (age, gender, ethnicity) trained on cropped faces without margins.
MusicBERT: Symbolic Music Understanding with Large-Scale Pre-Training
Inspired by the success of pre-training models in natural language processing, in this paper, we develop MusicBERT, a large-scale pre-trained model for music understanding.
Uncovering the Limits of Text-based Emotion Detection
Identifying emotions from text is crucial for a variety of real world tasks.
Minimax Filter: Learning to Preserve Privacy from Inference Attacks
The paper proposes a novel filter-based mechanism which preserves privacy of continuous and high-dimensional attributes against inference attacks.
Towards Wide Learning: Experiments in Healthcare
In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline.