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 implementations

Most implemented papers

EDA: Enriching Emotional Dialogue Acts using an Ensemble of Neural Annotators

bothe/EDAs LREC 2020

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

rajamohanharesh/Emotion-Recognition 22 Dec 2019

In this work, we conduct an extensive comparison of various approaches to speech based emotion recognition systems.

Cross-lingual Emotion Intensity Prediction

jbarnesspain/fine-grained_cross-lingual_emotion COLING (PEOPLES) 2020

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

WadeYin9712/SentiBERT ACL 2020

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

HSE-asavchenko/face-emotion-recognition 31 Mar 2021

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

HSE-asavchenko/face-emotion-recognition 31 Mar 2021

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

microsoft/muzic Findings (ACL) 2021

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

nur-ag/emotion-classification Findings (EMNLP) 2021

Identifying emotions from text is crucial for a variety of real world tasks.

Minimax Filter: Learning to Preserve Privacy from Inference Attacks

jihunhamm/MinimaxFilter 12 Oct 2016

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

sayakpaul/Generating-categories-from-arXiv-paper-titles 17 Dec 2016

In this paper, a Wide Learning architecture is proposed that attempts to automate the feature engineering portion of the machine learning (ML) pipeline.