## 2 Related Work
### Arabic Emotion Recognition Datasets  
The creation of labeled Arabic emotion recognition datasets has attracted a significant amount of attention in the literature. [PERSON] and [PERSON]'s study [58] introduced the multi-dialect DINA dataset for Arabic emotion analysis. This dataset, collected from Twitter, comprises 3000 tweets written in various dialects and is manually annotated according to [PERSON]'s six emotion categories: anger, disgust, fear, happiness, sadness, and surprise. Another study by [PERSON] and [PERSON] [39] presented the AETD dataset, primarily consisting of Egyptian dialect tweets. AETD includes 10,065 examples, each labeled with one of seven emotion tags: anger, fear, happiness, love, sadness, surprise, or sympathy, or none in case of the absence of emotion in the given example. Additionally,Alhuzali et al. (2020) introduced the LAMA dataset, which contains 7268 tweets covering [PERSON]'s eight basic emotions: anger, anticipation, disgust, fear, joy, sadness, surprise, and trust. The data collection process began with a seed set of 48 emotion-carrying expressions and regular expressions, resulting in the selection of 8000 tweets (1000 per emotion category) for manual review by annotators to remove duplicates, ultimately leading to the removal of 732 tweets from the final dataset.  
One of the earliest labeled datasets for Arabic emotion recognition was created through the SemEval-2018 Task 1: Affect in Tweets challenge ([PERSON] et al., 2019). The subtask for Arabic multi-label emotion classification used a labeled dataset from Twitter, which consisted of 4381 tweets written in multiple Arabic dialects and covered eleven emotions: anger, anticipation, disgust, fear, happiness, love, optimism, pessimism, sadness, surprise, and trust. Additionally, the study by [PERSON] et al. (2019) introduced the Iraqi Arabic Emotion Dataset (IAEDS), which comprised 1365 examples collected from Facebook posts and labeled with [PERSON]'s six basic emotion categories.  
Furthermore, the SenWave dataset, presented in the study by [PERSON] et al. (2019), includes 10,000 Arabic tweets annotated into ten categories. It was collected during the early months of the COVID-19 pandemic and encompasses a variety of Arabic dialects. The ArPanEmo dataset ([PERSON] et al., 2019) is another Arabic emotion recognition dataset, consisting of 11,128 online posts, each labeled with one of eleven categories: anger, anticipation, confusion, disgust, fear, joy, neutral, optimism, pessimism, sadness, and surprise. It primarily focuses on the Saudi dialect and addresses topics related to the COVID-19 pandemic collected from three sources: Twitter, YouTube, and online newspaper comments between March 2020 and March 2022.  
A comprehensive overview of Arabic emotion analysis, including available language models and emotion recognition resources, was presented in a recent study ([PERSON] et al., 2019). Table 1 provides a summary of existing datasets for Arabic emotion recognition that are manually or semi-automatically labeled.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline \multicolumn{5}{c}{**Details**} \\ \cline{2-6}
\begin{tabular}{c} 11 emotions: anger, \\ anticipation, disgust, fear, \\ happiness, love, \\ and \\ Arabic dialects \\ \end{tabular} \\ \hline \hline \end{tabular}
\end{table}
Table 1: Summary of existing Arabic emotion recognition datasets.