  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.
