Improving fine-grained emotion classification using LLMs through sequential learning of emotions

This study proposes an approach to improving emotion classification performance by introducing a Sequential Emotion Learning (SEL) method. Conventional learning methods often struggle with fine-grained emotion categories. To address this, the SEL approach first trains the model on seven basic emotions, which are relatively easier to classify due to their clear distinctions. The model is then fine-tuned using 24 more nuanced emotion labels, enhancing its ability to tackle complex emotion classification tasks. Experimental results suggest that the SEL method performs better than the baseline, achieving higher accuracy from the early stages of training. The SEL model also reaches its peak performance relatively quickly and shows improved classification capabilities on unseen, general sentences, indicating its robustness across different text scenarios. These results suggest that the SEL method can effectively improve emotion classification, particularly in tasks that require distinguishing complex emotions. This sequential learning approach offers a potential advantage over traditional methods and may be applied to other domains that involve intricate classification tasks. Future research can explore the generalizability of this method to other classification problems to further enhance its utility.