Emotion Classification Edit Task
Natural Language Processing • Text Classification
About
Given text, classify it as 'neutral or no emotion' or as one, or more, of several given emotions that best represent the mental state of the writer.
Benchmarks
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Datasets
Latest papers without code
AdCOFE: Advanced Contextual Feature Extraction in Conversations for emotion classification
Emotion recognition in conversations is an important step in various virtual chat bots which require opinion-based feedback, like in social media threads, online support and many more applications.
Affect Analysis in-the-wild: Valence-Arousal, Expressions, Action Units and a Unified Framework
Affect analysis and recognition can be seen as a dual knowledge generation problem, involving: i) creation of new, large and rich in-the-wild databases and ii) design and training of novel deep neural architectures that are able to analyse affect over these databases and to successfully generalise their performance on other datasets.
ACTION UNIT DETECTION EMOTION CLASSIFICATION FACIAL ACTION UNIT DETECTION
General-Purpose Speech Representation Learning through a Self-Supervised Multi-Granularity Framework
This paper presents a self-supervised learning framework, named MGF, for general-purpose speech representation learning.
EMOTION CLASSIFICATION LANGUAGE MODELLING REPRESENTATION LEARNING SELF-SUPERVISED LEARNING SPEECH RECOGNITION
Training an Emotion Detection Classifier using Frames from a Mobile Therapeutic Game for Children with Developmental Disorders
The classifier achieved 66. 9% balanced accuracy and 67. 4% F1-score on the entirety of CAFE as well as 79. 1% balanced accuracy and 78. 0% F1-score on CAFE Subset A, a subset containing at least 60% human agreement on emotions labels.
Exploring Deep Neural Networks and Transfer Learning for Analyzing Emotions in Tweets
In this paper, we present an experiment on using deep learning and transfer learning techniques for emotion analysis in tweets and suggest a method to interpret our deep learning models.
EMOTION CLASSIFICATION EMOTION RECOGNITION TRANSFER LEARNING
Emotional Semantics-Preserved and Feature-Aligned CycleGAN for Visual Emotion Adaptation
First, we generate an adapted domain to align the source and target domains on the pixel-level by improving CycleGAN with a multi-scale structured cycle-consistency loss.
EMOTION CLASSIFICATION EMOTION RECOGNITION UNSUPERVISED DOMAIN ADAPTATION
Investigating Emotion-Color Association in Deep Neural Networks
The representations learned by deep neural networks can indeed show an emotion-color association 2.
Combining Deep Transfer Learning with Signal-image Encoding for Multi-Modal Mental Wellbeing Classification
To address this problem, this paper proposes a framework to tackle the limitation in performing emotional state recognition on multiple multimodal datasets: 1) encoding multivariate time series data into coloured images; 2) leveraging pre-trained object recognition models to apply a Transfer Learning (TL) approach using the images from step 1; 3) utilising a 1D Convolutional Neural Network (CNN) to perform emotion classification from physiological data; 4) concatenating the pre-trained TL model with the 1D CNN.
4 EMOTION CLASSIFICATION OBJECT RECOGNITION TIME SERIES TRANSFER LEARNING
Domain Adaptation based Technique for Image Emotion Recognition using Pre-trained Facial Expression Recognition Models
In this paper, a domain adaptation based technique for recognizing the emotions in images containing facial, non-facial, and non-human components has been proposed.
DOMAIN ADAPTATION EMOTION CLASSIFICATION EMOTION RECOGNITION FACIAL EXPRESSION RECOGNITION
Experiencers, Stimuli, or Targets: Which Semantic Roles Enable Machine Learning to Infer the Emotions?
Is it a particular target (everybody loves X) or a stimulus (doing X makes everybody sad)?
EMOTION CLASSIFICATION EMOTION RECOGNITION SEMANTIC ROLE LABELING