## 2 Related Work
### Arabic Emotion Recognition  
Early research studies on Arabic emotion recognition relied on lexicons [35; 36; 37] and machine learning algorithms [38; 39; 40]. In these studies, emotion lexicons and machine learning algorithms, such as Naive Bayes, Sequential Minimal Optimization, and SVM [41], were employed to develop emotion classifiers. [PERSON] et al. [42]organized SemEval-2018 Task 1: Affect in Tweets, which included a subtask for multi-label emotion classification in Arabic. The study by [PERSON] et al. [43], which secured the top rank on the leaderboard, explored the use of three learning algorithms: Ridge Classification, Random Forests, SVM, and an ensemble of the three. They examined a set of features, including sentiment lexicons, n-grams, AraVec embeddings [44], and FastText embeddings [45]. The SVM classifier outperformed other models, while AraVec word embeddings outperformed other features, achieving accuracy rates of 48.9%, a micro average F1-score of 61.8%, and a macro average F1-score of 46.10%. [PERSON] et al. [40] conducted binary classification using a one-vs.-all SVM strategy to address the multi-label emotion classification subtask in the SemEval-2018 challenge. In this approach, a set of binary SVM classifiers, equal to the number of emotion labels, was built in parallel to recognize the emotions in the given texts. Their proposed system achieved an accuracy of 46.5%, a micro average F1-score of 59.7%, and a macro average F1-score of 44.6%. Deep learning models and various neural network architectures were explored for the identification of human emotions in Arabic text. [PERSON] and [PERSON] [46] addressed the multi-label Arabic emotion classification subtask in SemEval-2018 [42] as a binary classification problem. Their proposed neural network consisted of three hidden layers with a Rectified Linear Unit (RELU) activation function, and the output layer comprised a single sigmoid neuron predicting the intensity of the emotion on a scale from 0 to 1. This real-valued number, ranging from 0 to 1, was subsequently normalized to either 1 (indicating the presence of an emotion) if it exceeded 0.5 or 0 (indicating no emotion) if it fell below 0.5. They reported that their system achieved fourth place in the emotion classification subtask, with an accuracy rate of 44.60%. The study by [PERSON] et al. [47] proposed three models for Arabic emotion recognition in text: the Human-Engineered Feature-based (HEF) model, the Deep Feature-based (DF) model, and a hybrid model called HEF+DF. The HEF model employed various features, including domain-specific, stylistic, lexical, syntactic, and semantic features. Deep neural networks (DNNs) with ReLU and sigmoid activation functions were utilized in this model. The DF model, on the other hand, employed LSTM and Gated Recurrent Unit (GRU) deep models along with pretrained word embeddings, which included emoji2 vec [48], AraVec [44], Glove [49], and FastText [45; 50]. The study found that the hybrid of both models (HEF + DF) outperformed each individual model when evaluated on the Arabic Emotions Twitter Dataset (AETD). The HEF+DF model achieved an accuracy of 71.8%, with a weighted-average F1-score, precision, and recall of 71.8%, 72.2%, and 71.8%, respectively. Similar to other languages, PLMs have recently garnered significant attention due to their valuable contributions to state-of-the-art results in many NLP tasks. They serve as effective transfer learning techniques that can be fine-tuned on limited labeled data and produce excellent results for various downstream tasks [51; 52; 53; 54; 55]. Several studies have applied PLMs and transfer learning to Arabic emotion recognition in text [55; 56; 57]. The study by [PERSON] et al. [56] introduced ARBERT and MARBERT, two PLMs specifically designed for Arabic language understanding. MARBERT was pretrained on social media data, which included 1 billion Arabic tweets. In contrast, ARBERT was mainly pretrained on MSA datasets. These PLMs were evaluated on various social meaning tasks, including emotion recognition.