## 5 Experiments
### Results and Discussion
\begin{table}
\begin{tabular}{l c c c c c c c c c c} \hline \hline
**DL** & \multicolumn{3}{c}{**CNN**} & \multicolumn{3}{c}{**Bi-LSTM**} & \multicolumn{3}{c}{**CNN \& B-LSTM**} \\
**Class** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Emo & 50.2 & 43.8 & 42.1 & 41.5 & 37.1 & 34.9 & 53.2 & 45.7 & 43.6 \\ POS & 62.3 & 55.2 & 54.8 & 58.7 & 46.7 & 42.0 & 59.4 & 54.3 & 54.4 \\ Ling & 61.1 & 51.4 & 49.6 & 64.9 & 63.8 & 64.2 & 64.8 & 51.4 & 48.1 \\ Comb & 60.9 & 57.1 Generally, the results unveil a number of interesting findings of the benefits of feature extraction methods on Arabic satire classification. It is evident that combining word embeddings with ML or DL models does not result in an impressive performance, as it was consistently outperformed by unigram-based learned models. The reason for this is that simple and direct averaging in word embeddings does not take into account the order of the words, which is captured more precisely by n-gram models. In addition, we noticed that GloVe and Word2 Vec was the most effective word embedding for feature extraction when combined with ML and DL models. However, FasText produced comparable results, leading us to conclude that there is no specific form of word embedding that tops all others. This may be because AracVec, GloVe, and FastText were all trained on Arabic data from Twitter, Wikipedia, and multiple Arabic corpora that contain the plurality of formal Modern Arabic words, and are well suited to satirical articles written in a formal codified form. Although BERT outperformed GPT in terms of transformers, we utilized the basic model of GPT2, which may have provided superior performance if the superior model version had been utilized. Across all tests, the transformers BERT and GPT achieved high results of 95% and 85%, respectively. For textual features-built models, the best model reached 72.6%, which is in line with the study by [PERSON] et al. (2018), who achieved an F1-score of 73% with ML models. As for DL models, surprisingly, we found poor performance, i.e., 60%, which is in line with [91], which used sentiment lexicons to classify satirical content reaching a score of 46%. The approach of analyzing the textual content either as extracting textual features or sentiment features gave poor performances. This could be because satirical articles are highly dependent on the audience the articles are delivered to in terms of culture, gender, and context. Consequently, the textual content of satirical articles varies depending on their audience, resulting in diverse textual features that might have not been extracted in the feature extraction phase. Notably, from our comprehensive study, the highest F1-score (95.0%) was achieved with the BERT model. However, the second highest F1-score was achieved with the CNN model when the unigram was applied, which affirmed the efficacy of n-grams in the classification of Arabic satirical articles. This could be interpreted as the greater the n-grams length is, the less accurate the classification becomes. In other words, smaller n-grams tend to emphasize one unit of the text at a time, compared to higher n-grams, which take more than one unit at a time. This finding was also supported in other studies of text classification tasks [92]. Results of BERT and GPT models in Table 12.