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

arXiv:2403.06108v1 Announce Type: new
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 …

abstract arxiv augmentation challenges classification cs.ai cs.cl data dataset detection emotion emotion detection emotions fine-grained language language models large language large language models paper performance text transfer transfer learning type