# Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques
## 3 Proposed Methodology
### Data Set Description  
Our data set was obtained from Patient Experience Center of the Saudi MOH. A total of 21 spreadsheets were provided (file size is 0.59 GB), where each spreadsheet is composed of 1000-4200 manually labeled comments. The records represent responses to the PX surveys--in particular, the answers to the question \"do you have any further comments?\"--collected from many healthcare facilities across Saudi Arabia. This implies the possibility of them containing different dialects. The entries represent comments collected from primary healthcare centers, inpatient, emergency room (ER), and outpatients. Each record is labeled in terms of sentiment and classification of the comment according to the SHCT [21]. Figure 4 shows the top 100 words present in the data set. \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** \\  & & & & & **Model** \\ \hline Joshi 2021 [8] & English & Linear SVC & \begin{tabular}{l} Healthcare (drug \\ reviews) \\ \end{tabular} & Multi-class & Fl: 88\% R: 88\% P: 88\% \\ Khanbahi 2022 [9] & English & SVM & \begin{tabular}{l} Healthcare (PX) \\ \end{tabular} & Multi-class & A: 62\%+ \\ AL-laith 2021 [35] & Arabic & LSTM & \begin{tabular}{l} Social Media \\ \end{tabular} & Multi-class & A: 75\% \\ Faris 2021 [36] & Arabic & BiLSTM & \
\begin{tabular}{l} Healthcare (Altibbi) \\ \end{tabular} & Multi-class & A: 87.2\% P: (83-95\%) \\ Biniz 2018 [26] & Arabic & CNN & News & Multi-class & A: 92.94\% \\ Aliwy 2020 [42] & Arabic &
\begin{tabular}{l} Ensemble (voting combining LR, NB, DT) \\ \end{tabular} & NADH data set & Multi-class & F1: 20.05\% \\ El-rifai 2021 [23] & Arabic & CNN-GRU & News & Multi-label & A: 94.85\% Fl: 78.86\% \\ Alsukhni 2021 [32] & Arabic & LSTM & News & Multi-label & F1: 83.8\% \\ [PERSON] 2021 [38] & Arabic & Linear SVC & Social Media & Multi-label & A: 81.44\%, F1: 92.0\% \\ Elnagar 2020 [39] & Arabic & Attention-GRU & News & Multi-label & Multi: A 88.86\% \\ Ghourabi 2021 [45] & Arabic & AraBERT & News & Multi-label & A: 85.1\% Fl: 86.42\% \\ Faris 2022 [50] & Arabic & BiLSTM & Healthcare (Altibbi) & Multi-label & F1: 35.46\% \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Comparison of multi-class classification models in the reviewed literature. Table legend: A, accuracy; R, recall; P, precision; F, F1 score. \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** \\  & & & & & **Model** \\ \hline El-rifai 2021 [23] & Arabic & CNN-GRU & News & Multi-label & A: 94.85\% Fl: 78.86\% \\ Alsukhni 2021 [32] & Arabic & LSTM & News & Multi-label & F1: 83.8\% \\ Omar 2021 [38] & Arabic & Linear SVC & Social Media & Multi-label & A: 81.44\%\%, F1: 92.0\% \\ Elnagar 2020 [39] & Arabic & Attention-GRU & News & Multi-label & R: 90.5\% P: 93.52\% \\ Ghourabi 2021 [45] & Arabic & AraBERT & News & Multi-label & Multi: A: 88.86\% \\ Faris 2022 [50] & Arabic & BiLSTM & Healthcare (Altibbi) & Multi-label & R: 54.4\%, F2: 26.8\%, \\  & & & & & & F1: 35.46\% \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of multi-label classification models in the reviewed literature. Table legend: A, accuracy; R, recall; P, precision; F, F1 score. In 2019, the MOH developed the Saudi healthcare complaints taxonomy (SHCT) [21] in order to standardize the classification of patient comments for better analysis. This hierarchical taxonomy provides four levels for classifying patient comments. At the top, the taxonomy begins with the domain, in which there are three main domains: Clinical complaints, relationship complaints, and management complaints. Under each domain comes the second level of the taxonomy, called the category, with seven categories.