# Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques
## 2 Related Work
### Text Classification in Arabic  
For the Arabic language, many works have focused on text classification. The characteristics of the Arabic language require exploring the relevant approaches considered, as well as comparison of the efficiency for each approach. [PERSON] et al. [23] developed a binary and multi-label classifiers to classify news data. For binary classification, a data set of 90k articles was used to train several ML algorithms, including LR, KNN, DT, and SVM, where the latter excelled with an F1 score of 97.9%. For the multi-labeled approaches, the data set used comprised 290,000 articles. They adopted shallow-learning techniques as well as neural network techniques, and found that an architecture combining a CNN with a gated recurrent unit (GRU) provided the best result, with an accuracy of 94.85%. Likewise, [PERSON] et al. [24] utilized news data sets and trained their proposed CNN with genetic algorithm (GA) for hyperparameter tuning as a classifier. The authors introduced the Saudi Newspaper Articles data set (SNAD), which contains 45,000 articles in 6 classes. For comparison, the authors utilized the Moroccan News Articles Data set (MNAD) [25], which contains 111,000 articles in 5 classes. The CNN model using the SNAD data set obtained 84% accuracy, while the CNN with the GA optimization resulted in higher accuracy of 88%. The authors also compared their work to a previous work [26] that used the MNAD data set, and found that the result of using global vectors for word representation (GloVe) [27] enhanced the accuracy. On the other hand, [PERSON] et al. [28] investigated classification of the DA for three dialects of interest: Egyptian, Gulf, and Levantine. The researchers applied deep learning approaches for DA classification and used a subset of the Arabic Online Commentary (AOC) data set [29], which contains 30,000 entries (10,000 for each dialect of interest). The author tested LSTM, CNN, Bidirectional LSTM, and convolutional LSTM models, and found that the LSTM performed best among the others, with 71.4% accuracy. In addition, [PERSON] [30] also focused on DA--specifically, Levantine sub-dialects, which include Palestinian, Jordanian, Lebanese and Syrian. The data set used for their experiment was collected from Twitter and constituted 100,000 tweets, and the author utilized other corpora to extract dialect-specific features. Finally, an SVM model was trained and evaluated with 10-fold cross validation, resulting in 65% accuracy. In the same way, [PERSON] et al. [10] also concentrated on the DA of the Gulf dialects, in order to conduct binary classification of tweets (as either spam or not). Figure 2: Summary of the related work. They used a data set comprised of 2000 tweets from the Gulf region. They considered the use of other features extracted from tweets, such as URLs, hashtags, and a list of profanity words. The authors implemented NB and SVM, where NB delivered better results when combined with the extracted features, obtaining an accuracy of 86%. Additionally, [PERSON] et al. [11] aimed to classify cyber-bullying within a set of 32,000 deleted comments from an Arabic news website. In their experiment, they utilized AraVec word embeddings [31] with the CNN, LSTM, and GRU combined, which achieved an F1 score of 84%; they also implemented ML approaches and obtained competitive results. On the other hand, [PERSON] et al. [32] utilized a deep learning approach for their proposed models. They employed a multi-layer perceptron (MLP) and an RNN with LSTM to be trained on the Mowjaz data set [33] (of size 9500), which is a multi-labeled data set consisting of 10 classes. The RNN with LSTM surpassed the MLP model, with an F1 score of 82.03%. Furthermore, [PERSON] et al. [34] focused on SMS spam detection, using a deep learning approach that combines a CNN with LSTM and other ML classifiers for benchmarking. The data consisted of 5574 English SMSs and 2730 Arabic SMSs, annotated with two labels as either spam or not, and the researcher developed two types of model.