Large Language Models on Fine-grained Emotion Detection Dataset with Data Augmentation and Transfer Learning

TL;DR

This paper explores improving emotion detection in text using large language models, data augmentation, and transfer learning on the GoEmotions dataset, aiming to better identify subtle emotions in NLP applications.

Contribution

It introduces methods combining data augmentation and transfer learning to enhance fine-grained emotion classification performance.

Findings

Improved emotion detection accuracy on GoEmotions dataset

Insights into challenges of subtle emotion classification

Directions for future research in emotion detection

Abstract

This paper delves into enhancing the classification performance on the GoEmotions dataset, a large, manually annotated dataset for emotion detection in text. The primary goal of this paper is to address the challenges of detecting subtle emotions in text, a complex issue in Natural Language Processing (NLP) with significant practical applications. The findings offer valuable insights into addressing the challenges of emotion detection in text and suggest directions for future research, including the potential for a survey paper that synthesizes methods and performances across various datasets in this domain.