# The Effect of Preprocessing on Arabic Document Categorization
## 2 Related Work
[PERSON] _et al._[12] studied the impact of stop word removal on Arabic document categorization by using different algorithms, and concluded that the SVM with sequential minimal optimization achieved the highest accuracy and lowest error rate. [PERSON] and [PERSON] [13] introduced a novel stemmer called the Educated Text Stemmer (ETS) for stemming Arabic documentation. The ETS stemmer is evaluated by comparison with output from human generated stemming and the stemming weight technique (Khoja stemmer). The authors concluded that the ETS stemmer is more efficient than the Khoja stemmer. The study also showed that disregarding Arabic stop words can be highly effective and provided a significant improvement in processing Arabic documents. [PERSON] [14] developed a new Arabic light stemmer called P-stemmer, a modified version of one of [PERSON]'s light stemmers. He showed that his approach to stemming significantly enhanced the results for Arabic document categorization, when using naive Bayes (NB), SVM, and random forest classifiers. His experiments showed that SVM performed better than the other two classifiers. [PERSON] _et al._[15] studied the impact of stemming on Arabic document categorization. In their study, two stemming approaches, namely, light stemming and word clusters, were used to investigate the impact of stemming. They reported that the light stemming approach improved the accuracy of the classifier more than the other approach. [PERSON] and [PERSON] [16] investigated classification techniques with a large and diverse dataset. These techniques include a wide range of classification algorithms, term selection methods, and representation schemes. For preprocessing tasks, normalization and stop word removal techniques were used. For term selection, their best average result was achieved using the GSS method with term frequency (TF) as the base for calculations. For term weighting functions, their study concluded that length term collection (LTC) was the best performer, followed by Boolean and term frequency collection (TFC). Their experiments also showed that the SVM classifier outperformed the other algorithms. [PERSON] _et al._[17] discussed different variations of vector space model (VSM) to classify Arabic documents using the k-nearest neighbour (KNN) algorithm; these variations are Cosine coefficient, Dice coefficient, and Jacaard coefficient and use the inverse document frequency (IDF) term weighting method for comparison purposes. Normalization and stop word removal techniques were suggested as preprocessing tasks in their experiment. Their experimental results showed that the Cosine coefficient outperformed Dice and Jaccard coefficients. [PERSON] _et al._[18] developed a hybrid system for Arabic document categorization based on the semantic vicinity of terms and the use of a radial basis modeling. Normalization, stop word removal, and stemming techniques were used as preprocessing tasks. They adopted the hybridization of _N-gram_ + _TFiDF_ statistical measures to calculate similarity between words. By comparing the obtained results, they found that the use of radial basis functions improved the performance of the system. Most of the previous studies on Arabic DC have proposed the use of preprocessing tasks to reduce the dimensionality of feature vectors without comprehensively examining their contribution in promoting the effectiveness of the DC system, which makes this study a unique one that addressed the impact of preprocessing tasks on the performance of Arabic classification systems. To clarify the differences of the present work from the previous studies, the investigated preprocessing tasks and experimental settings are comparatively presented in Table 1. In this table, normalization, stop word removal, and stemming are abbreviated as NR, SR, and ST, respectively. The experimental settings include feature selection (FS in the table), dataset language, and classification algorithms (CA in the table).