## 1 Introduction  
Sentiment analysis is considered a natural language processing (NLP) method for analyzing users' orientations toward services and topics under consideration. The goal of sentiment analysis mechanisms is to differentiate between subjective and objective sentiments. Objective sentiment is used to express general facts, while subjective sentiment is based on polarity terms that express user reviews or opinions. Objective sentences are excluded during the analysis of sentiments, whereas subjective sentiments can be classified into positive, negative, or neutral polarities. Most peoples' and users' feelings towards different topics are reflected on social media reviews and sites [1]. Social media allow users to share their views, opinions, and emotions to classify the main service and enhance its specifications in the future. The Arabic language is widely applied on most social media platforms, such as Twitter and Facebook. The Arabic language is considered the official language of Middle East countries and North Africa, comprising 27 countries in addition to the other countries that consider the Arabic language one of its popularly used dialects. It has recently attracted more attention due to the increasing use of Arabic in social media platforms [2]. In addition, the Arabic language contains different dialects with high morphological meanings that can be categorized as standard Arabic and colloquial. Most Twitter users, especially those writing and speaking in English, can express their opinions using traditional or colloquial sentences. They can also use mixed terms that make the preprocessing and analysis processes more complex [3]. Since the Arabic language contains a vast number of linguistics and terminology that are challenging to clean and analyze, sentiment analysis has recently attracted a lot of attention. In addition, sentiment analysis and prediction become more difficult with free writing on social media, particularly in the Arabic language [4]. Few research methodologies are conducted to analyze Arabic sentiments on social media due to the high morphological linguistics in each Arabic sentence that is difficult to classify and analyze. Arabic Social Media Analysis for Arabic (ASAD) aims to fill an important gap in analyzing social media in the Arabic language [5]. Marketing analysis of services or products and public responses to events, persons, and pandemics are considered major examples of the dire need to analyze Arabic sentiments efficiently and accurately, especially when the dataset is new and has not been trained earlier [6]. Recent research methodologies of sentiment analysis depend mainly on collecting datasets from social media such as Facebook and Twitter that provide expressive sentiments from several domains. Twitter users create huge volumes of text to convey their views [7] with a wide range of terms, fields, services, and products [8; 9; 10]. Twitter datasets are collected from different sources and categories for classifying public events, pandemics, and product marketing [11; 12; 13]. The analysis of sentiments on the Twitter dataset has gained more interest as large companies and institutions depend mainly on user reviews to enhance and upgrade their business services. In addition, the simplicity of the Twitter platform makes it one of the most powerful social networks in the world, with a high volume of daily-generated sentiments [14]. Sentiment analysis methodologies concentrate on mining texts and sentences that can explore deep visions and insights into users' attitudes and opinions. The main analysis strategy is extracting, classifying, and analyzing the sentiments related to several categories, such as emotional, cognition, social, and theoretical, and analyzing complex texts. Furthermore, most retrieved texts from the Twitter dataset contain unstructured texts that need more preprocessing steps to become more concise and clearer. This can increase the complexity of the selected analysis methodology [15].