Analysis of Basic Emotions in Texts Based on BERT Vector Representation

TL;DR

This paper introduces a GAN-based model for emotion recognition in text, focusing on generating synthetic datasets of emotion combinations from incomplete labeled data to improve emotion analysis.

Contribution

It presents a novel GAN-type approach for creating comprehensive synthetic emotion datasets from limited labeled text data.

Findings

Successful generation of synthetic emotion datasets

Enhanced emotion recognition accuracy

Effective handling of incomplete labeled data

Abstract

In the following paper the authors present a GAN-type model and the most important stages of its development for the task of emotion recognition in text. In particular, we propose an approach for generating a synthetic dataset of all possible emotions combinations based on manually labelled incomplete data.