# Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine
## 4 Dataset  
The dataset used in this study was collected from PSAU, specifically from the College of Computer Engineering and Sciences (CCES) via student course evaluation surveys post-COVID-19 (refer to Figure 1). The data was gathered by the quality unit of CCES, resulting in a total of 384 records and 30 features from 30 different courses spreading across 66 sections. Figure 2 provides a sample of the dataset used. The features include both categorical and quantitative variables, as well as textual comments. Table 1 provide a translation of the Arabic comments provided in Figure 2. Table 2 provides an overview of the extracted features and their descriptions. We highlight that the proportion of class labels in our dataset refers to the distribution of different categories or classifications within the data. For instance, in a sentiment analysis task, the class labels is mainly considered to be \"positive\", \"negative\", and \"neutral\", each corresponding to the sentiment expressed in a piece of text. The proportion of these labels would then indicate how many of the entries in the dataset are positive, negative, or neutral, typically represented as a percentage or a count. The proportions of each class within the dataset are as following:  
* Positive Class: Significantly more than half of the data, 57.1%. * Neutral Class: Roughly around half the number of Positive samples, 28.6%. * Negative Class: Roughly around a quarter of the number of Positive samples, 14.3%. Figure 1: Sample of students’ course evaluation survey. Figure 2: Sample of students’ comments. \begin{table}
\begin{tabular}{l c} \hline \hline
**Review Translated** & **Sentiment** \\ \hline Dr. [PERSON] has facilitated the course content and was very supportive.. & Positive \\ \hline The professor explanation was very good and understandable. & Positive \\ \hline The professor. & Neutral \\ \hline The professor is amazing. & Neutral \\ \hline I like the professor. When I graduate, I will be a database administrator. & Positive \\ \hline The slides are fair but need improvement. & Neutral \\ \hline Slides are not clear. & Negative \\ \hline Slides are good. I think thet deserve to contain more details. & Neutral \\ \hline Explanation. & Neutral \\ \hline Exams are quite difficult. & Neutral \\ \hline We are pressed by the labs and homeworks. & Negative \\ \hline More effort to reach a certificate in Business Architecture. & Positive \\ \hline Team work and I think I found great what we are learning in programming. & Negative \\ \hline The variety of tasks and the presence of a real challenge in the work is & Positive \\ what makes the experience unique and valuable. & Positive \\ \hline Practical. & Neutral \\ \hline Benefit. & Negative \\ \hline Idea on organizing information. & Neutral \\ \hline Lot of chapters. & Negative \\ \hline Despite the quantity of theoretical courses that require remembering more & Positive \\ than understanding, I was able to understand clearly and it was funny. & Negative \\ \hline Quizzes are hard. & Negative \\ \hline \hline I am quite scared from quizzes that we take at the beginning of lectures. & Negative \\ \hline \end{tabular}
\end{table} 
Table 1: Translation of students comments illustrated in Figure 2.