## 2 Related Work  
Social media is increasing in popularity at a very fast pace, and so is the number of users, which leads to huge amounts of unstructured text posted on these platforms. Besides exchanging information, users on social media also express their ideas and opinions about different topics, such as products, travel, food, and even politics and people. Consequently, users express their sentiments about people, organizations, places, or ideas. Among them, Twitter is a social media platform which generates large amounts of text containing political insights, which can be analyzed to extract the users' opinions and predict trends in the elections.  
Social networks have the potential to revolutionize the world of social science research, or Social Science Engineering [4]. Twitter is mainly used by specialists because of its text-oriented content. The popularity of the Twitter platform is growing both socially and scientifically, and its content is used in more and more varied research.  
In addition to using tweets for Topic Classifying applications, they are very popular as a research resource for Sentiment Analysis. This topic is related to extracting the positive or negative perception from a text document, and according to [5], tweet posts were used because it was concluded that the analysis on shorter and concise texts is more efficient and offers better performance. This type of analysis is often found in services such as movie recommendations, products, reviews, ranking, and sorting by preferences.  
In [6], a framework that aggregated user interactions on social for demographics and personality discovery tasks was proposed. The authors of [7] developed a study in which customers reacted to a retailer's political opinion posted on a social media account.  
In addition, according to [8], in the field of natural language processing, research studies are conducted on emoticons and hashtags used in tweets, as these elements also provide significant informational load for the content. The paper concludes that using the correct hashtags in a tweet post already frames that post in a certain class, and links it to a certain trend.  
In [9], the textual analysis of the data on Twitter is made based on full-text, hashtags, and data clusters and the purpose of the paper is to classify tweets according to the Democrat and Republican political orientation of the users. The use of neural networks as a prediction mechanism in the aforementioned paper leads to better performance indices than in the case of probabilistic algorithms, resulting in a final prediction accuracy of about 94%.  
An intelligent threatening language detection that uses machine learning and deep learning models is described in [10]. The authors of [11] developed a deep learning-based system used for information triaging on Twitter during several situations, such as emergency situations or influential events. [PERSON] et al. [12] classified the posts on Twitter using a natural language processing approach and an explainable artificial intelligence model.  
[PERSON] presented the study of transcripts in the parliamentary debates in the United States of America. Using only the written form of politicians' natural language as input data, the study investigates a politician's agreement or disagreement on a political proposal, depending on the words he or she uses to express his or her opinion. Of course, on average, the agreement or disagreement of a political representative on a respective political proposal was in line with the ideologies of the party to which he belonged. This is, in fact, also a verification step in the context of NLP.  
In [13], the authors proposed a methodology to predict the outcome of election results by utilizing the user influence factor, using a combined approach between Principal Component Analysis and SVM.  
This research examines the performance of various forms of the Naive Bayes algorithm in detecting the political orientation (Democrat and Republican), based on tweets.