# Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques
## 2 Related Work
### Summary  
In the literature, the text classification task has been tackled using various approaches. Traditional ML algorithms have been widely investigated for this purpose, and SVM and its variations (e.g., Linear SVC) have demonstrated substantial improvements in performance [5; 8; 23; 30; 37; 38]. NB classifiers have also been employed for this task [10]. The success of deep learning in many fields has led to the adoption of deep learning algorithms for NLP. CNN--either stand-alone or augmented with other techniques--has shown notably good performance for text classification [6; 11; 12; 24; 26; 34]. It can be observed that RNNs are not widely used for problems that require sequential dependencies, and many text classification research has used their variations, LSTM/BiLSTM and GRU/BiGRU instead, either stand-alone or in combination with other algorithms [6; 11; 23; 28; 32; 34; 35; 36; 39; 50]. When an RNN is used, it is often combined with other algorithms [6; 12].  
The use of BERT for Arabic language text was recently investigated using news and Twitter data sets, and improved text classification results were achieved using AraBERT [45; 47; 48; 49; 51]. Tables 1-3 summarize the related literature focused on binary, multi-class, and multi-label text classification, respectively. Promising results were obtained using DL and AraBERT-based approaches for the classification of Arabic text in many applications; however, to the best of our knowledge, these approaches have not been tested in the context of Arabic PX.  
\begin{table}
\begin{tabular}{l l l l l l} \hline \hline
**Reference** & **Language** & **Best Model** & **Data Set Source** & **Classification Type** & **Result of the Best** \\ \hline Tafri 2019 [6] & English & \begin{tabular}{l} Ensemble (CNN, RNN, \\ LSTM) \\ \end{tabular} & Healthcare (PPM) & Binary & F1: 89.9\% \\ Nawab 2020 [7] & English & \begin{tabular}{l} Deep learning Sequential \\ Model \\ \end{tabular} & PX survey & Binary & F1: 81\% A: 82\% \\ [PERSON] 2017 [5] & Russian & \begin{tabular}{l} Linear SVM \\ \end{tabular} & Healthcare (drug & Binary & F1: 73.3\% \\ El-rifai 2021 [23] & Arabic & SVM & News & Binary & F1: 97.93\% A: 97.9\% \\ Alsaleh 2021 [24] & Arabic & CNN with GA & News data sets & Binary & SNADA : 88.71\% \\ Lulu 2018 [28] & Arabic & LSTM & Social Media & Binary & A: 71.4\% \\ Wray 2018 [30] & Arabic & SVM & Social Media & Binary & A: 65\% \\ Alorini 2019 [10] & Arabic & Naive Bayes & Social Media & Binary & A: 86\% F1: 92\% P: 81\% \\ Rachid 2020 [11] & Arabic & \begin{tabular}{l} Combination of CNN, \\ LSTM, GRU \\ \end{tabular} & Social Media & Binary & F: 84\% \\ Ghourabi 2020 [34] & Arabic & Hybrid CNN–LSTM & SMS text & Binary & F: 95.39\% F1: 91.48\% \\ Ikram 2019 [37] & Arabic & SVM & Legal text & Binary & A: 98.11\% F1: 98.04\% \\ Omar 2021 [38] & Arabic & SVC & Social Media & Binary & A: 97.8\% F1: 97.79\% \\ Elnagar 2020 [39] & Arabic & Attention-GRU & News & Binary & F: 97.9\% \\ Alhawrat 2020 [40] & Arabic & Multi-kernel CNN & Miscellaneous & Binary & A: 95.94\% \\ Ameur 2020 [12] & Arabic & Combined (RNN–CNN) & Online Source Arabic & F1: 98.61\%, P: 98.63\% \\  & & & Corpora & & R: 98.58\% \\ Touati-Hamad 2022 [44] & Arabic & Hybrid CNN–LSTM & Quran, Arabic Learner & Binary & F1: 97.86\% A: 98.33\% \\  & & & Corpus & & P: 97.86\% R: 97.86\% \\ Djandj 2020 [47] & Arabic & AraBERT with MTL & Twitter & Binary & F1: 90.15\% (offensive) \\ Althabit 2021 [48] & Arabic & \
\begin{tabular}{l} AraBERT with TanH \\ function \\ \end{tabular} & Twitter & Binary & F1: 83.41\% (hat-speech) \\ Faraj 2021 [49] & Arabic &
\begin{tabular}{l} AraBERT with TanH \\ with AraBERT \\ \end{tabular} & Twitter & Binary & A: 68\% (AraBERTv0.2) \\  & & Ensemble (hard-voting) & ArSarcasm-v2 data set & Binary & F1: 59.8\% A: 78.3\% \\ Uyangodage 2021 [51] & Arabic & AraBERT & Twitter & Binary & F1: 69.8\% \\ \hline \hline \end{tabular}
\end{table}
Table 1: Comparison of binary classification models in the reviewed literature. Table legend: A, accuracy; R, recall; P, precision; F, F1 score.