## 5 Experiments
### Results and Discussion  
Based on data collection, preprocessing, feature extraction, and model training, it was observed that the dataset was well-balanced with a moderate number of samples. The data were preprocessed by scaling and splitting into training and validation sets. Various machine learning models were trained, including SVM, LR, NB, and RF, and their performances were evaluated. Additionally, a CNN, Bi-LSTM, and a hybrid CNN & BiLSTM neural network, along with transformers BERT and GPT, were tested and evaluated. First, n-grams were used to train the models as shown in Tables 6 and 7. We observes that both ML and DL models performed better with unigram compared to bigram and trigram. Of both models, DL with unigram reached the highest performance. The highest F1-score achieved (94.1%) was obtained with the CNN model. Then, three textual feature categories were evaluated: emotions, part-of-speech, linguistics, and the combination of them. We used them with ML and DL models to determine the best classification model for satirical articles. The results are shown in Tables 8 and 9. ML models gave promising results; SVM and RF were the best models with linguistics features and with all features combined. The highest F1-score (72.7%) was achieved with RF with all features combined. However, DL surprisingly performed worst with textual features. As the highest F1-score achieved was 64.2% when linguistic features were used with the Bi-LSTM model. Word embeddings, Word2 vec, FastText, and GloVe were used to train ML and DL models. Notably, Word2 Vec and GloVe performed better than FastText, as shown in Tables 10 and 11. The best-performing ML model was SVM with GloVe embeddings, which reached an F1-score of 91%. The highest F1-score was achieved with CNN & Bi-LSTM when W2 Vec embedding was used, reaching 94%. Under the same word embeddings, we found that DL models and ML models performed very similarly. Finally, BERT and GPT transformers were utilized, since they are among the most prevalent classification models in NLP currently. BERT performed better than GPT with an F1-score of 90%. \begin{table}
\begin{tabular}{l 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 \& Bi-LSTM**} \\
**Class** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Fast & 89.8 & 89.8 & 89.8 & 86.2 & 86.2 & 86.2 & 84.2.8 & 84.2 & 84.2 \\ W2V & 86.1 & 96.0 & 91.0 & 83.1 & 89.0 & 86.0 & 92.0 & 96.0 & 94.0 \\ GloVe & 86.0 & 91.0 & 89.0 & 73.0 & 95.0 & 83.0 & 96.0 & 90.0 & 91.0 \\ \hline \hline \end{tabular} \({}^{\dagger}\) Fast = FastText, W2V= Word2 Vec.  
\end{table} 
Table 11: Results of deep learning algorithms with word embeddings \({}^{1}\). \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 \& Bi-LSTM**} \\
**Class** & **P** & **R** & **F1** & **P** & **R** & **F1** & **P** & **R** & **F1** \\ \hline Uni & 94.4 & 95.2 & 94.1 & 94.2 & 91.4 & 93.7 & 89.1 & 97.3 & 93.5 \\ Bi & 63.1 & 99.1 & 77.4 & 70.1 & 91.1 & 79.0 & 74.2 & 99.0 & 85.3 \\ Tri & 70.1 & 64.2 & 67.6 & 53.1 & 92.7 & 67.3 & 99.0 & 30.2 & 46.3 \\ \hline \hline \end{tabular} \({}^{1}\) Uni = Unigram, Bi = Bigram, Tri = Trigram.  
\end{table} 
Table 7: Results of the deep learning algorithms with n-grams \({}^{1}\).