# The Effect of Preprocessing on Arabic Document Categorization
## 5 Experiment and Results
### Experimental Results and Analysis  
First, we ran three different experiments to study the effect of each of the preprocessing tasks. The three experiments were conducted using four different representations of the same dataset. The original dataset without any preprocessing task was used in the first experiment, and this experiment was presented as the baseline. In the second experiment, normalization was used as the preprocessing task of documents. The stop word removal was used in the third experiment. In the fourth and last experiment, we used light stemmer [27]. The results of the experiments for preprocessing task when applied individually on the three classification algorithms are illustrated in Table 5 and Figure 2.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Classification** & **Feature** & **Baseline** & **Normalization** & **Stop Word** & **Light** \\
**Algorithm** & **Size** & **(BS)** & **(NR)** & **Removal (SR)** & **Stemmer (LS)** \\ \hline \multirow{6}{*}{**NB**} & 50 & 0.65 & 0.6678 & 0.6511 & 0.7311 \\  & 100 & 0.7737 & 0.7763 & 0.7881 & 0.8107 \\  & 500 & 0.8841 & 0.8852 & 0.8896 & 0.88  
According to the proposed method, applying normalization, stop word removal, and light stemming has a positive impact on classification accuracy in general. As shown in Table 3, applying light stemming alone has a dominant impact and provided a significant improvement in classification accuracy with SVM and KNN classifiers but has a negative impact when used with NB classifiers and when feature size increases. Similarly, stop word removal provided a significant improvement in classification accuracy with NB and SVM classifiers, but has a negative impact when used with KNN classifiers and when feature size increases. The latter conclusion is surprising because most studies on DC in literature apply stop words directly by assuming them irrelevant. The results showed that the normalization helped to improve the performance and provided a slight improvement in the classification accuracy. Therefore, normalization should be applied without depending on feature size and classification algorithms. Given that normalization helps in grouping the words that contain the same meaning, a smaller amount of features with further discrimination are achieved. However, stop word removal and stemming status may change depending on the feature size and the classification algorithms. In the following experiments, we studied the effect of combining preprocessing tasks on the accuracy of Arabic document categorization. All possible combinations of the preprocessing tasks listed in Table 6 are considered during the experiments to reveal all possible interactions between the preprocessing tasks. Stop word removal, Normalization, Light stemming (abbreviated in Table 6 as NR, SR, and LS, respectively) are either 1 or 0 which refer to \"apply\" or \"not-apply\". In these experiments, three classification algorithms, namely, SVMs, NB, and KNN, were applied to find the most suitable combination of preprocessing tasks and classification approaches to deal with Arabic documents. The results of the experiments on the three classification algorithms are illustrated in Table 7 and Figure 3. The minimum and maximum Micro-F1 and the corresponding combinations of preprocessing tasks at different feature sizes are also included. \begin{table}
\begin{tabular}{c c c} \hline \hline
**No.** & \multicolumn{3}{c}{**Preprocessing Tasks Combinations**} \\ \hline
1 & Normalization (NR):1 \(|\) Stop-word removal (SR):1 \(|\) Stemming (LS):0 \\
2 & Normalization (NR):1 \(|\) Stop-word removal (SR):0 \(|\) Stemming (LS):1 \\
3 & Normalization (NR):0 \(|\) Stop-word removal (SR):1 \(|\) Stemming (LS):1 \\
4 & Normalization (NR):1 \(|\) Stop-word removal (SR):1 \(|\) Stemming (LS):1 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Combinations of preprocessing tasks. Figure 2: Experimental results preprocessing task using NB, KNN, SVM Classifiers. 0.2248 in KNN classifier, and between 0.0044 and 0.0808 in SVM classifier.