## 1 Introduction  
Throughout Arab nations, social networking communication is immensely famous in recent generations as a means of publicly conveying one's views on a variety of motifs. It is estimated that there are far more than 11 million active Twitter accounts in the Arab region, and Saudi Arabia has the most active members with around 2.6 million. As an increasing number of individuals turn to social media sites to convey personal views and seek advice, demand for social media analysis intensifies. Experts in sentiment evaluation are particularly interested in assessing the community mood and spotting the latest tendencies in this area. Arabic social networking sites' sentiment analysis is particularly difficult since it must confront Arabic's complicated semantics, which is amplified by online posts in nonstandardized Arabic idioms and perhaps not following conventional linguistic forms [1]. Using acronyms and idioms to work around Twitter's 280-character limit set is a common tactic for those who want to quickly express their point [2; 3]. The use of abbreviations on Twitter sometimes leads to ambiguity in the interpretation of TTs, and these TTs also frequently involve multiple misspelled words and casual language rules. Throughout this research, the sentiment analysis of particular topics in social-network statistics is utilized to comprehend how views are disseminated to benefit social research analysts in tracking the influence of social challenges on individuals' attitudes, emotions, and momentum-building trends. The majority of sentiment analysis techniques are lexical (linguistic) or ML (statistical) in nature. Machine-learning (ML) algorithms, including support vector machines (SVM), naive Bayes (NB), and decision trees (DT) are used to analyze retrieved textual features. These approaches were developed on the text that had been prelabeled by sentiment polarization. Sentiment assessment requires the creation of robust sentiment LX in the context of the sentiment classification domain, which includes a particular set of words having predefined polarities. Once these unfamiliar terms are identified, the statistical-semantical grading of these terms and their placement can be used to estimate the polarity of an entire paragraph. Tremendous human effort is required to manually gather thought words to create a strong LX in lexical techniques. Techniques for studying modern standard Arabic (MSA) and dialectic Arabic (DA) appear in the literature. Studies on ML techniques use a variety of different ML methodologies [4; 5; 6; 7]. Sentiment studies for MSA and the lexical technique were investigated in existing work [8; 9; 10; 11]. Dialect sentiment analysis, on the other hand, has received minimal research attention. It is difficult to conduct sentiment analysis on Saudi Twitter posts, since there is no gold-labeled dataset or vocabulary that incorporates all Saudi Arabian accents, including Hejazi (western area), Najdi (central area), Shamali (northern area), Janubi (southern area), and Sharqawa (southern area, eastern region). This poses a significant barrier in the study of Saudi Arabia's TTs. Saudi Arabian dialects such as Hejazi and Nejdi are composed of many dialectal variations even in a similar country. This shows how diverse Arabic dialects are and is a significant hurdle to NLP efforts in Saudi Arabia. MSA's nafiftha (window) is taqa in the Hejazi dialect, but it is shobak in the Nejdi dialect [12; 13; 14; 15]. The Arabic language is morphologically rich, with a significant amount of information about syntactic parts and relationships expressed at the word level. In the Arabic language, one word may have numerous distinct surface forms; nonetheless, every word could have a large number of forms. Furthermore, most Arabic names are derived from Arabic adjectives that are frequently misconstrued with feelings. Arabic words with the same root might have contradictory emotional orientations due to the usage of diacritics and rich morphology.