# Location Analysis for Arabic COVID-19 Twitter Data Using Enhanced Dialect Identification Models
## 2 Related Work  
The identification of Arabic dialects is based on both spoken [14; 15; 16] and written form [12; 13; 17; 18; 19; 20; 21]. Much of early research has focused on distinguishing between MSA and EGY forms. For example, in [17], [PERSON] and [PERSON] suggested a supervised method for the task of sentence-level MSA-EGY identification by exploiting a subset of the Arabic Online Commentary (AOC) dataset. They studied whether pre-processing affects the performance of the classifier or not. The authors found that this was helpful in some cases. They reported an accuracy of 85.5% using 10-fold cross-validation with an SVM classifier. In [12], [PERSON] and [PERSON] tested a word trigram model for MSA, EGY, GLF, and LEV sentences. They achieved an accuracy of 77.8% to classify tweets between EGY and MSA. In the case of (EGY vs. GLF vs. LEV), an accuracy of 83.3% was achieved. The accuracy in the case of the 4-way classification task (MSA, EGY, GLF, and LEV) was 69.4% [22].  
In [23], [PERSON] et al. suggested a system that identifies linguistic code-switching for the MSA-EGY classification task. The authors adjusted their system in [18] and used a word-based 5-gram model. They utilized a morphological analyzer to annotate the dataset. For annotation, they used MADAMIRA that has been proposed in [24]. The authors improved system in [18] achieved an accuracy of 87.7% when distinguishing between EGY and MSA. In [21], [PERSON] et al. proposed a work that is similar to these systems in focusing on the classification problem of MSA-EGY for Twitter data. They collected a dataset of 880 K tweets to train the model and used 700 tweets for testing. The authors investigated several morphological and lexical features. They reported a 10% absolute gain over the models trained solely on n-grams.  
In [25], [PERSON] et al. proposed an ensemble model consisting of a group of several classifiers using a set of features that includes word-level and character-level Term Frequency Inverse Document Frequency (TF-IDF) features, class probabilities of many linear classifiers, and probabilities of a language model [26]. Also, [PERSON] and [PERSON] in [27] presented MICHAEL, a lightweight approach developed for domain dialect identification. Their proposed method used character-level features and perform classification without any pre-processing. They used character N-grams extracted from the original sentences to train a multinomial naive Bayes classifier. MICHAEL achieved an accuracy of 53.25% with N equals to 1, 2, or 3 and accuracy of 62.17% with character 4-grams. In [28], [PERSON] and  
Figure 1: The map of Arabic dialect groups distribution.  
[PERSON] proposed systems that are based on language modeling. They used language models to extract features that are later utilized as an input for other machine learning methods. In [29] [PERSON] et al. used a classification method based on symmetric Kullback-Leibler For discriminating between dialects in a multi-dialect context.  
[PERSON] [30] concentrated on the AOC categories for the 4-way classification task (MSA, EGY, GLF, and LEV). The author claimed that a simple word-level n-gram model trained on the manually annotated portion of AOC as well as unannotated Facebook data improved classification accuracy. On 10% of the manually annotated AOC dataset, [PERSON] [30] reported an accuracy of 87.8%. In [13], [PERSON] and [PERSON] proposed a model that handles the binary (MSA, non-MSA), 3-way (EGY, GLF, and LEV), and 4-way (MSA, EGY, GLF, and LEV) classification tasks. They also used the AOC data and reported an accuracy of 87.23%, 87.41%, and 82.45% for binary, 3-way, and 4-way classification tasks, respectively. It is worth mentioning that [PERSON]'s [30] result is based on AOC data plus other unannotated data. However, the segmentation of data and benchmark results on AOC is not available. While using AOC in our work, the results of [PERSON]'s [30] works are not comparable to our results.