# Text Classification of Patient Experience Comments in Saudi Dialect Using Deep Learning Techniques
## 1 Introduction  
In the healthcare sector, measuring the _patient experience_ (PX) is a topic of major interest that plays an important role in the safety and well-being of individuals. The Beryl Institute defines PX as \"the sum of all interactions, shaped by an organization's culture, that influence patient perceptions across the continuum of care\" [1]. Measuring patient experience is important for several reasons. First, it provides valuable feedback on the quality of care provided by healthcare organizations, helping them to identify areas for improvement. Second, it can improve patient satisfaction and engagement, leading to better health outcomes, as well as relieving the burden on medical workers [2]. Finally, it can inform healthcare policy and decision making, ensuring that patient-centric care is prioritized. According to the _Patient Access Leadership Research Report_ released by Lumcon in 2021, 90% of survey participants believe that PX is the primary differentiator for hospitals [3]. In the Kingdom of Saudi Arabia, the Ministry of Health (MOH) pays great attention to measuring the patient experience, and a program was launched in 2018 to measure patient experience [4]. The goals of this program include raising the quality of healthcare services, aiding decision makers with insightful reports about patient experience, and developingstandards for patient experience that comply with regional and international standards. Patient experience information is typically gathered using surveys. The survey questions are close-ended with five-point scales, allowing for the measurement of their degree of satisfaction. In addition, there is also an open-ended question that allows the patient to express freely their opinions about their experience during the last visit. This open-ended question is formatted as \"please provide additional comments\", and the response is mostly free text in Arabic. Machine learning (ML) algorithms have been widely adopted for text classification in various healthcare fields, including for the analysis of patient experience data [5; 6; 7; 8; 9]. However, existing research in the literature has mainly focused on the English language. As such, at present, patient comments written in Arabic are difficult to automatically analyze using traditional machine learning and statistical approaches. Dealing with Arabic text is challenging for many reasons; for example, it has complex grammar due to the presence of different forms of words based on gender, singular, plural, and dual contexts. In addition, there are many dialectal variations, where each has its own morphology, phonology, syntax, and lexical forms [10; 11]. Many studies have demonstrated the difficulties associated to processing Arabic text, and some researchers have attributed this fact to the richness and complexity of the Arabic morphology [11; 12]. The current approach used by the Saudi MOH is manually categorizing comments based on a pre-defined set of classes. With the huge number of patient comments received from health facilities across the country (i.e., hundreds of thousands), it is difficult to draw insights in a timely manner, thus slowing down the process of addressing patient concerns. Manual classification is a tedious task and humans are subject to making errors; it is also a very time-consuming task and can demand immense effort. Deep learning (DL) approaches have recently demonstrated success in many fields, such as image recognition, object detection, speech processing, and text classification [13]. Compared to traditional ML, DL models are able to automatically extract complex features from input data. The use of convolutional neural networks (CNNs) and recurrent neural networks (RNNs) has significantly enhanced the state of the art in text classification [14].