## 2 Literature Review  
Several studies have been conducted on sentiment analysis using various algorithms in different languages such as Indonesian [9], Urdu [13], Russian [20], and Arabic [21]. However, most work has been carried out in English, such as [22; 23; 24]. The study of natural language processing is interesting for two reasons, firstly due to the algorithm's variations, and secondly due to language variety. A successful framework for a particular language may need adjustment when it comes to implementation in a different language. Sometimes, even dialect can heavily affect the likely result. In Arabic, for example, the researchers in [25] divide the variety of languages into three groups: Classical Arabic (CA), Modern Standard Arabic (MSA) and Dialectical Arabic (DA). Sentiment analysis is a classification task of input text into several classes. The number of classes varies from binary (positive, negative), three classes (binary with neutral) and complex systems of five classes ranging from very positive to very negative. Arabic is one of the most popular languages in the world, with 330 million native speakers, and is one of the official languages of the United Nations (UN) [26]. There are some public datasets available in the Arabic language of various classes. Some public datasets consist of positive and negative classes such as the Large-Scale Arabic Book Review [27] and Ar-Twitter, proposed by [28]. The rest of the available dataset consists of four more classes, such as [29], which proposed four classes, and ArsenTb, which employs five classes [10; 30; 31]. The sentiment analysis is mainly divided into lexicon-based and supervised machine learning approaches. Lexicon-based approaches rely on the sentiment word dictionary. Some researchers have claimed promising results using lexicon-based approaches, such as [32; 33]. Supervised approaches rely on machine learning algorithms and are based on a labeled corpus; therefore, they are also referred to as corpus approaches. The machine learning algorithms are basically trained by the labeled corpus to build a model. There are many researchers who have employed a variety of languages and algorithms. The lexicon approach was quite popular at the beginning of sentiment analysis study. It is powerful and can be performed using simpler algorithms. However, it is mainly based on the availability of lexicon dictionaries. A pre-defined dictionary provides a set of words in each sentiment polarity. A document is classified into a particular polarity on the basis of the word frequencies of each polarity side from the dictionary. Ref. [34] compared six sentiment opinion lexicons, and proposed a new general-purpose sentiment lexicon that they claimed was able to achieve 69% accuracy when determining the sentiment of news headline. In sentiment analysis in the Arabic language, ref. [32] proposed a sentiment lexicon with a size of 16,800 words. Their experiment claimed that the integrated lexicon achieved better results, at 74% accuracy, than manual and dictionary-based sentiment analysis. Later on, ref. [33] proposed a sentiment lexicon consisting of 120,000 Arabic terms. They claimed an even better accuracy of 86.89%. The lexicon approach generally achieves better performance when the sentiment lexicon is complete. However, as a language is not static knowledge, this approach needs to be continuously updating the data all the time. Therefore, researchers have tried to find a better way of overcoming the language dynamics. Corpus-based methods rely on real-time data such as reviews, social media, and the web. By annotating the real-time data, data researchers train the machine to recognize the patterns of each sentiment polarity. The supervised approach relies on machine learning algorithms such as support vector machines, decision trees, logistic regression, and many more. Ref.