# Sentiment Analysis of Arabic Course Reviews of a Saudi University Using Support Vector Machine
## 2 Related Work
### SVM for Sentimental Analysis  
The impact of machine learning [22; 23] on the education system and how it is affecting human learners and learning is discussed recently in [24]. the authors compared deep learning in computers and humans and highlight the importance of explainability and accountability in machine learning systems. The article suggested that students should have an understanding of machine learning systems and opportunities to adapt and create them. It recommends changes to school curricula to address these issues and gives recommendations for teachers, students, policymakers, developers, and researchers. The analysis of the literature review on SVM for sentimental analysis in education reveals that there is no specific focus on the use of SVM for analyzing real-life data of Arabic review texts collected from students in higher education. However, there are several research articles that have used SVM for sentimental analysis in other domains and languages. One example is [25] a review and comparative analysis over social media is provided. The authors highlighted the use of SVM to classify sentiment in social media posts written in English. The authors highlighted that SVM performed well in comparison to other machine learning algorithms such as Naive Bayes and K-NN. Another example has used SVM to classify sentiment in user reviews written in English. The authors found that SVM performed well in comparison to other machine learning algorithms such as Naive Bayes, K-NN and Decision Tree [26]. In [27] researchers presented state-of-the-art approaches for aspect-based sentiment analysis (ABSA) of Arabic hotel reviews, using supervised machine learning techniques, including deep recurrent neural networks (RNN) and SVM. The study used various features, such as lexical, word, syntactic, morphological, and semantic, to train and evaluate the models. Results showed that the SVM approach outperformed the deep RNN approach for the tasks of aspect category identification, aspect opinion target expression extraction, and aspect sentiment polarity identification. However, the deep RNN approach had a faster execution time, particularly for the second task. It's worth noting that there are several papers that use SVM on Arabic sentiment analysis but the data are not always real-life data which is an Arabic review text collected from students in higher education. For example, the authors in [28] focused on analyzing sentiment in e-learning using traditional machine learning algorithms, specifically SVM and Naive Bayes. They used a dataset of 2000 tweets related to King Abdul-Aziz University to train and evaluate their models. For example in [29], the authors proposed Arabic Language Sentiment Analysis (ALSA). ALSA is a challenging task due to the complex nature of the Arabic language. The language has multiple levels such as phonetics, morphology, syntax, lexicology, semantics, and figurative nature. These levels have positive and negative connotations and meanings that need to be considered in the analysis. ALSA has been less studied compared to the analysis of opinions and feelings in English and Indo-European languages. Therefore, there is a need for a comprehensive and full proposal of a strategy for ALSA. This includes analyzing opinions and feelings at all levels of language, in addition to the importance of building an annotated corpus that helps to understand an Arabic sentence from the level of phonetics to the rhetorical and metonymy levels. In conclusion, while there is a lack of literature specifically focused on the use of SVM for sentimental analysis of real-life data of Arabic review texts collected from students in higher education, there are several research articles that have used SVM for sentimental analysis in other domainsand languages. Therefore, it is possible that SVM could be used for sentimental analysis of Arabic review texts collected from students in higher education, but more research is needed to confirm this.