. Textual fine-grained emotion detection is a challenging task that has yet to achieve powerful performance in both pretrained language models (PLMs) and large language models (LLMs). In this paper, we analyze a fine-emotion dataset and current approaches to provide insight of existing issues. We propose the idea of treating fine-emotion detection as having multiple appropriate answers, and to consider annotator level labels instead of the golden label. Annotator labels are labels provided by individual annotators before being aggregated into the golden (reference) label. These labels highlight the subjectivity of individual annotators before the labels were aggregated. We then evaluate treating neutral label separately and using LLMs as aid for mistake filtering and augmentation. We show that using annotator labels instead of allows BERT model to predict different interpretations without being penalized despite the weaker performance. Large potential has yet to be explored on annotator level label fine-emotion detection and we provide several ideas through evaluating approaches and analyzing the results. We hope to encourage a change in how fine emotion is detected, multiple accurate annotator labels, even within a multi-label scenario.