Analysis of Basic Emotions in Texts Based on BERT Vector Representation
arXiv:2101.11433v2
Originality Synthesis-oriented
AI Analysis
This addresses emotion analysis in text for applications like sentiment analysis, but it appears incremental as it builds on existing GAN and BERT methods without claiming major breakthroughs.
The authors tackled the problem of emotion recognition in text by developing a GAN-type model to generate a synthetic dataset of all possible emotion combinations from incomplete labeled data, but no concrete results or numbers are provided.
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.