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
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Latest papers
A Video Is Worth 4096 Tokens: Verbalize Videos To Understand Them In Zero Shot
Multimedia content, such as advertisements and story videos, exhibit a rich blend of creativity and multiple modalities.
NUAA-QMUL-AIIT at Memotion 3: Multi-modal Fusion with Squeeze-and-Excitation for Internet Meme Emotion Analysis
This paper describes the participation of our NUAA-QMUL-AIIT team in the Memotion 3 shared task on meme emotion analysis.
StockEmotions: Discover Investor Emotions for Financial Sentiment Analysis and Multivariate Time Series
There has been growing interest in applying NLP techniques in the financial domain, however, resources are extremely limited.
Is Style All You Need? Dependencies Between Emotion and GST-based Speaker Recognition
On the task of speech emotion detection, we obtain 80. 8% ACC with acted emotion samples from CREMA-D, 81. 2% ACC with semi-natural emotion samples in IEMOCAP, and 66. 9% ACC with natural emotion samples in MSP-Podcast.
MARLIN: Masked Autoencoder for facial video Representation LearnINg
This paper proposes a self-supervised approach to learn universal facial representations from videos, that can transfer across a variety of facial analysis tasks such as Facial Attribute Recognition (FAR), Facial Expression Recognition (FER), DeepFake Detection (DFD), and Lip Synchronization (LS).
Improved acoustic-to-articulatory inversion using representations from pretrained self-supervised learning models
In this work, we investigate the effectiveness of pretrained Self-Supervised Learning (SSL) features for learning the mapping for acoustic to articulatory inversion (AAI).
Transformer-based Text Classification on Unified Bangla Multi-class Emotion Corpus
In this research, we propose a complete set of approaches for identifying and extracting emotions from Bangla texts.
Natural Language Inference Prompts for Zero-shot Emotion Classification in Text across Corpora
This raises the question how to prompt a natural language inference model for zero-shot learning emotion classification.
KAM -- a Kernel Attention Module for Emotion Classification with EEG Data
In this work, a kernel attention module is presented for the task of EEG-based emotion classification with neural networks.
A Monotonicity Constrained Attention Module for Emotion Classification with Limited EEG Data
In this work, a parameter-efficient attention module is presented for emotion classification using a limited, or relatively small, number of electroencephalogram (EEG) signals.