Textual fine-emotion detection is a challenging task that has yet to achieve powerful performance in both Language model (LM) and Large Language models (LLM). 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. We then evaluated treating neutral label separately and using LLM as aid for mistake filtering and augmentation. We show that using annotator labels instead of golden label 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 the approaches evaluated and the analysis of these approaches. We hope to encourage a change in how fine-emotion detection is detected, allowing multiple accurate answers instead of one.