## 2 Related Work
### Arabic Emotion Recognition
The emotion model built by fine-tuning MARBERT exhibited the best performance when evaluated on the AraNET\({}_{\text{Emo}}\) dataset, achieving an F1-score of 75.18%. The study by [PERSON] et al. [55] illustrated the training of a Transformer model called QARiB, compared QARiB with three existing BERT models, and provided best practices for training language models. The paper presented various approaches for training five different QARiB models that varied in terms of preprocessing steps, the included Arabic varieties, and the size of the training data. The training data encompassed formal texts from Arabic newswire sources and informal texts from Twitter. Various preprocessing techniques were experimented with, including tokenization using Farasa, removal of diacritical marks and Kashida (word elongation), and normalization of different forms of Arabic letters. The QARiB PLMs were evaluated on various downstream tasks, including emotion recognition. The best-performing model achieved an F1-score of 46.80% when evaluated on the SemEval 2018 emotion classification dataset. The study revealed that the inclusion of both formal (MSA) and informal (diacritical Arabic) texts led to the best-performing models in downstream tasks. Additionally, the paper reported that word segmentation improved the model's performance on the named entity recognition task but not on emotion recognition. The use of transfer learning for Arabic emotion recognition was investigated in a recent study by [PERSON] et al. [57]. The study also examined the incorporation of semantic information, including named entities and sentiments extracted from text, before fine-tuning the pretrained BERT-based model. Two methods were investigated: embedding the semantic information within the text and using an SEP token to differentiate the input text from the added semantic information. The proposed methods were thoroughly evaluated for various PLMs. The results indicated that including semantic information in the text before fine-tuning the model enhanced the performance of the PLM, regardless of the differences or similarities between the pretrained and fine-tuned data. The paper demonstrated that incorporating only named entity information yielded better results than incorporating only sentiment information. The proposed method was evaluated on the AETD dataset, resulting in an accuracy of 81.73%, a weighted-average F1-score of 81.60%, a precision of 81.92%, and a recall of 81.40%. From the literature on Arabic emotion recognition, we can observe several notable issues related to fine-grained text classification in general and Arabic emotion recognition in particular. Fine-grained emotion recognition requires a large number of labeled data examples per emotion category to obtain good results, especially when working with deep learning models. Moreover, the degree of nuance required in fine-grained Arabic emotion recognition can vary from one application to another. For instance, some studies focused on six emotion categories, while others delved into 11 emotion categories in their investigations. In fact, one study [23] on English emotion recognition created labeled data for 27 emotion categories. These varying requirements from one study to another necessitate a significant amount of time and effort to collect and label more data or to modify existing datasets.