# Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques
## 2 Related Work
### Text Classification in Arabic
For the machine learning approach, the authors chose term frequency-inverse document frequency (TF-IDF). Alternatively, for the deep learning approach, they used a word embedding model to accommodate the mixed-language data set. A hybrid CNN-LSTM model was then developed, which obtained an accuracy of 98.37%, a precision of 95.39%, a recall of 87.87%, and an F1 score of 91.48%, outperforming the other ML approaches. [PERSON] et al. [35] aimed to review a huge number of tweets with the intention of grasping the public's emotions associated with COVID-19. The authors collected 5,500,000 tweets labeled with six labels for the emotions, along with two classes for the presence of symptoms or not. The researcher utilized word embeddings with an LSTM model, and the symptom classification obtained an accuracy of 75%. Interestingly, [PERSON] et al. [36] proposed a solution to classify medical consultations in Arabic. The authors utilized a data set from the Altibibi telemedicine company, consisting of 1,500,000 consultations, with 75,000 among them labeled and falling under fifteen classes. The authors used many word embedding approaches, and found that developing their own led to the best result. After that, they employed LSTM and BiLSTM models with different variations, in terms of the number of units. The best result was obtained by the BiLSTM with 30 units, which delivered a precision ranging from 83% to 95% for each class. [PERSON] et al. [37] examined documents in the legal field using a data set of 1452 Arabic documents from the Moroccan Supreme Court for two classes: real estate or traffic. The authors chose four different models for classification of the data: KNN, NB, DT, and SVM with TF-IDF vectorized data. After implementation of the models, they found that SVM delivered the best results, with an accuracy of 98.11% and average F1 score of 98.04%. In addition, [PERSON] et al. [26] proposed a news classifier using a data set collected from Moroccan news websites, consisting of 111,000 articles falling within five classes. A CNN model was developed, and its hyperparameters were configured to obtain the best output, which resulted in an accuracy of 92.94%. Furthermore, [PERSON] et al. [38] developed a multi-label Arabic text classification model, along with a binary topic classification model, using a Twitter and Facebook data set. The data set, with 44,000 entries in total, includes 4000 entries for each of the eleven classes. Many ML models were developed, among which the linear SVC achieved the best accuracy (of 97.8%) for binary classification, and also achieved the best accuracy (of 81.44%) when combined with TF-IDF in the multi-label experiment. [PERSON] et al. [39] also proposed solutions for both binary and multi-labeled classification problems. They created two data sets: the Single-Label Arabic News Articles data set (SANAD), which is annotated with single labels and composed of 110,000 entries; and the multi-label News Articles Data set in Arabic (NADiA), which consists of 450,000 entries and is multi-labeled with 28 labels. For binary classification, the authors implemented nine deep neural network algorithms. The best model among them was the attention-GRU, with an accuracy of 96.94%. For the multi-label classification, attention-GRU also achieved the best accuracy (of 88.68%) on 10 classes. [PERSON] et al. [40] proposed an approach that relies on a multikernel CNN. A total of 15 Arabic data sets with text of different lengths were used. The authors used Arabic Wikipedia fast-text pre-trained word vectors. Then, they trained a multi-kernel CNN model, which achieved an accuracy of 97.58% to 99.90% on the different data sets. [PERSON] et al. [12] proposed a model that combines the CNN and RNN using the Open-Source Arabic Corpora (OSAC) data set [41], which contains 21,000 documents in 10 classes. Then, they developed many models, and the best result was obtained by the combined RNN-CNN model with static word embeddings, which achieved an F1 score of 98.61%.