## 2 Background
### Feature Extraction Techniques
#### 2.2.1 Traditional Techniques  
* **N-grams**: a string of n-syllables that are contiguous. By collecting the most common n-grams rather than the whole corpus, this method might be used to obtain a more accurate categorization [18]. The n-grams approach was used with several standard algorithmic combinations to identify satire within the Arabic language [19]. Moreover, the n-grams method was used with other methods such as TF-IDF to achieve higher prediction results in classification tasks [20]. N-grams can be used in language processing to analyze patterns and relationships between words or phrases in a text. This method can provide useful insights into the satirical content found as forms of exaggeration or humorous words, i.e., the more humorous words found, the more likely the article would be classified as satiric.  
* **TF-IDF**: Term Frequency and Inverse Document Frequency. Here, a term's frequency in a corpus indicates how significant a role it plays in the text [18; 21]. It is simple to compute and express word similarity, but the semantic problems that impact the algorithm's overall performance make it ineffective [22]. Satire detection in Arabic was performed using the TF-IDF approach in several studies, such as those that utilized to reflect tweets syntactic structure [23] or linguistic features [24]. Generally, the TF-IDF approach has been proven to be effective in various natural language processing tasks, such as sentiment analysis and text classification [25]. Furthermore, researchers have also explored the use of TF-IDF in combination with machine learning algorithms to improve the accuracy of satirical detection models [26; 27]. Since TF-IDF is a numerical statistic used in information retrieval to determine the importance of a word in a document, it is a useful feature to detect the occurrence of satirical cues in a document. As the frequency of satirical elements increases, the probability of the model correctly classifying the piece as satire also increases.
* **Textual features**: addresses lexical, syntactic, and stylistic features. These attributes were useful in detecting satirical contexts in Arabic [28]. For instance, the authors of [29] employed stylistic elements that included quote marks, exclamation points, and questions to classify satirical text. Additionally, other features such as sentiment features, shifters, and contextual features were used for Arabic satire identification [28]. Regarding the shifters, they pick up a variety of linguistic phenomena, such as claims that are inconsistent with reality. They also detect instances of exaggeration, such as when people use the strong descriptive words \"huge\" and \"gigantic\". These linguistic phenomena help identify instances of Arabic satire, as they indicate a deviation from the norm or an intentional exaggeration. By analyzing surface/stylistic features, sentiment features, and contextual features, the authors in [28] were able to accurately identify and classify instances of satire in Arabic texts. These findings highlight the importance of considering various linguistic cues and markers when studying irony in different languages. As stated earlier, satirical articles have certain language characteristics, such as exaggeration, humor, and the use of conflicting terminology, which effectively convey the satirist's message. These linguistic cues presented as textual characteristics may be employed to determine the presence of satire within a given content.