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.
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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 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.
This paper presents a self-supervised learning framework, named MGF, for general-purpose speech representation learning.
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.
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.
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.
The representations learned by deep neural networks can indeed show an emotion-color association 2.
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.
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.