## 5 Discussion  
Our work has demonstrated the feasibility and potential of zero-shot classification in the context of building a fine-grained emotion detection dataset for literature analysis. Unlike previous research that focused on the direct use of zero-shot classification, we combined it with common semi-supervised learning approaches to construct a dataset for training supervised models. By leveraging the zero-shot NLI model, we ranked a large set of unsupervised sentences for each emotion and selected the most likely positive examples as weakly labeled data. These examples, along with their negative counterparts, were utilized to build binary pseudo-labeling models. By classifying all examples using these emotion-specific pseudo-labeling models, we obtained a multi-label dataset. To the best of our knowledge, this is the first publicly released dataset for multi-label fine-grained emotion detection in literature, with the most extensive emotion taxonomy of any dataset available, incorporating emotion labels not present in any other dataset. This is also the largest emotion detection dataset available and is two orders of magnitude bigger than the previously available literature dataset (Tales).  
In the realm of emotion detection, our study showcases the possibility of having a fine-grained emotion detection dataset specifically tailored for literature. The supervised models trained on our dataset achieved promising results on our human-labeled gold dataset, even without substantial adaptation for handling noisy data and without hyperparameter tuning. The F1 score of 0.59 (Table 8) is similar to what is expected with crowdsourced multi-label emotion datasets (see Section 1.3 and Table 11). This is the case despite the fact that our dataset has more fine-grained labels and thus would be expected to be more challenging to model. It is an unexpected result that a semi-supervised dataset would provide results this good, especially even before adding any techniques for handling noise. This validates our semi-supervised approach.  
The results comparing RoBERTa to BERT and the different model sizes (Table 10) suggest diminishing returns from increasing the quality of the underlying pretrained large language model, which can suggest better returns for focusing efforts on improving the dataset (e.g., filtering noisy examples) or the ability to learn from it (e.g., handling noisy labels).  
Furthermore, our experiments in transfer learning, including zero-shot (Section 4.3) and few-shot scenarios (Section 4.4), reveal the transferability of the learned emotion detection capabilities. Models trained on our automatically generated dataset perform comparably to models trained on GoEmotions, a crowdsourced dataset. In the zero-shot scenario, for the literature dataset Tales, we achieve 93% of the Cross-Validation F1-score (Table 13). In the few-shot fine-tuning scenario, transfer models trained on our dataset surpass the models trained only on the Tales examples until the 200 examples threshold (Table 14). Both these transfer learning experiments further help validate the quality of our dataset and thus validate the approach used to create it.  
Although the use of soft labels did not improve the results when evaluated on the gold subset of our dataset, all transfer experiments show superior results for the model trained with soft labels, implying better transferability with soft labels. If the ultimate use of any model trained on our dataset is transfer learning, using the soft labels seems preferable.