## 2 Related Work
### Fine-Grained Emotion Recognition  
Fine-grained emotion recognition in text has obtained significant attention in recent years, with ongoing research being focused on developing accurate and effective methods. Deep learning and Pretrained Language Models (PLMs) have been extensively explored in recent research studies [23; 24; 25], as these models provide a robust foundation for fine-grained emotion recognition by capturing intricate patterns and contextual cues. The study by [PERSON] et al. [26] employed various classifiers, including Naive Bayes, Support Vector Machine (SVM), and BERT, for 9-class emotion recognition in both English and Polish. The BERT-based model outperformed the conventional ML methods, achieving an accuracy of nearly 80% for emotion recognition in English and approximately 75% in Polish.  
The study conducted by [27] proposed a deep learning-based model for 7-class emotion recognition during the COVID-19 pandemic. The model relied on combining a Long Short-Term Memory (LSTM) model and the BERT model. Specifically, the model consists of BERT as the top layer, followed by a dropout layer. The output is then fed into an LSTM layer, followed by several dropout and dense layers. The model was evaluated on a benchmark dataset, achieving an accuracy and F1-score of 83% and 72%, respectively. [PERSON] et al. [28] explored the efficiency of BERT, DistilBERT, Generalized Auto-regression. Pretraining for Language Understanding (XLNet) [29], and Robustly Optimized BERT Pretraining Approach (RoBERTa) [30] models in recognizing emotions from texts. Using the same hyperparameters, the accuracies of the models, in decreasing order, were 74.31%, 72.99%, 70.09%, and 66.93% for RoBERTa, XLNet, BERT, and DistilBERT, respectively.  
A recent study by [PERSON] et al. [31] introduced a novel dual-channel method for emotion recognition in text. The architecture of the proposed method comprises several modules. Firstly, the embedding module utilizes the BERT model in order to extract textual features from input sentences in the form of embedding vectors. Subsequently, the dual-channel module takes the output of the embedding module as input and transfers it to two network channels, which consist of a CNN and bidirectional LSTM (BiLSTM). The embedding vectors from both channels are concatenated and then fed into the next module, which is the emotion classification module. This module learns and projects the emotion embeddings onto a hyperplane, forming clusters. The proposed method was examined using four different emotion recognition datasets, consisting of examples categorized into emotion classes ranging from 5 to 7 emotions. The method showed consistent performance across the four different datasets, with accuracy ranging from 72.89% to 80.67% and F1-scores ranging from 71% to 83%.  
Fine-grained emotion recognition and its potential applications often require taxonomies that encompass a considerable number of emotions, including nuanced states. This need for detailed emotion categories is vital for a deeper understanding of emotional content. Consequently, the development of more comprehensive taxonomies has recently attracted research attention. [PERSON] et al. [23] proposed a new taxonomy by introducing the GoEmotions dataset, which contains 58K English Reddit comments labeled as 27 emotion categories or neutral. The study by [PERSON] et al. [24] created an emotion dataset from an online health community, annotated with 8 fine-grained emotions. Indeed, the field of fine-grained emotion recognition has witnessed the emergence of several datasets that vary in size, domain, and taxonomy [32; 33; 34]. In general, emotion taxonomies and specialized labeled data continue to evolve and expand, paralleling the increasing potential applications for emotion recognition.