# Kashif: A Chrome Extension for Classifying Arabic Content on Web Pages Using Machine Learning
## 4 Results and Discussion  
This section discussed the models' performance. We have recorded the accuracy for each classifier with each feature selection technique, as in Table 9.  
Figure 7: Backend and frontend structure. Overall, the accuracies without using the feature selection technique are higher than the others, especially in XGBoost, RF, and DT. Moreover, XGBoost and RF have almost the same results for each feature selection technique, and the SVM and DT have almost the same results for each feature selection. XGBoost and RF outperform SVM and DT because both have the built-in capability to handle imbalanced datasets [40, 41], and they have used the concept of the ensemble method. Ensemble methods are an ML technique that combines several classifiers to produce one optimal predictive model. XGBoost has a boosting nature that uses ensemble techniques. Therefore, XGBoost inherits the ensemble techniques. The RF is the ensemble of the DTs, and it builds a forest of many random DTs. Moreover, algorithms work differently. For example, the DT classifier is built as a tree-like structure that reflects all possible outcomes of a decision depending on specified conditions, whereas the SVM uses a separator line between the categories, and the data are transformed to draw the separator, as in Figure 8.  
To set up the model that was deployed in the Kashif extension, we took the decision based on three standards: 1--high accuracy, 2--containing content-based features that include an analysis of the Arabic text in the web page, 3--maintaining the speed of extraction and prediction of the result. The first choice was XGBoost model, which achieved the highest accuracy at 95.55% without using a feature selection method. However, extracting and predicting data can be time-consuming, which is an issue because users generally expect the system to work quickly. The second choice is XGBoost with chi-square, which achieved an accuracy of 92.71%. However, the chi-square method did not choose the content features that matter about the Arabic content; it included the content features included in Table 3, such as the meta count, which does not contain any Arabic-content analysis. In other words, the chi-square method did not choose features that analyze Arabic words, such as counting lengthy popular keywords and counting repeated popular keywords inside the body tag. The third choice was to manually add two features that met our standards to the chi-square set since it has the highest accuracies among the other methods and fewer sets of features. The features added to it are the count of lengthy popular keywords and the count of repeated popular keywords (based on Google Trends) inside the body tag. Counting repeated popular keywords and lengthy popular keywords within the body of Arabic  
Figure 8: The separator line in the SVM. \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Technique/Model** & **XGBoost** & **RF** & **SVM** & **DT** \\ \hline ANOVA & 88.27\% & 87.53\% & 89.96\% & 87.04\% \\ \hline Chi-square & 92.72\% & 92.35\% & 83.70\% & 87.53\% \\ \hline Chi-square after & & & & \\ adding 2 more & 92.22\% & 92.96\% & 83.70\% & 86.42\% \\ features & & & & \\ \hline Correlation & 91.60\% & 92.22\% & 62.84\% & 86.30\% \\ \hline All features & & & & \\ without FS & 95.80\% & 94.81\% & 64.57\% & 88.02\% \\ \hline \hline \end{tabular}
\end{table} 
Table 9: Model performance (accuracy). websites, based on Google Trends data, is significant. These features were not commonly used before for Arabic websites, as they provide insights into the relevance and engagement of content. Website owners can tailor their content to match user search intent and improve search engine rankings by analyzing the keyword frequency and length based on trending topics. Then, we compared the results and found that the RF model with the selected features by chi-square and two more features achieved the highest accuracy of 92.96%.