# The Effect of Preprocessing on Arabic Document Categorization
## 2 Related Work  
DC is a high valued research area, wherein several approaches have been proposed to improve the accuracy and performance of document categorization methods. This section summarizes what has been achieved on document categorization from various pieces of the literature. For the English language, the impact of preprocessing on document categorization are investigated by [PERSON] _et al._[PERSON] _et al._ (2015). It is concluded that the influence of stop-word removal and stemming are small. However, it is suggested to apply stop-word removal and stemming in order to reduce the dimensionality of feature space and promote the efficiency of the document categorization system. [PERSON] _et al._[PERSON] _et al._ (2015) examined the impacts of normalization (stemming or lemmatization) and stop-word removal on English and Czech datasets. It is concluded that stop-word removal improved the classification accuracy in most cases. On the other hand, the impact of word normalization (stemming and lemmatization) was negative. It is suggested that applying stop-word removal and ignoring word normalization can be the best choice for document categorization. Furthermore, the impact of preprocessing tasks including stop-word removal, and stemming on the English language are studied on trimmed versions of Reuters 21,578, Newsgroups and Springer by [PERSON] _et al._[PERSON] _et al._ (2015). It is concluded that using stemming and stop-word removal has very little impact on the overall classification results. [PERSON] _et al._[PERSON] _et al._ (2015) examined the impact of preprocessing tasks such as tokenization, stop-word removal, lowercase conversion, and stemming on the performance of the document categorization system. In their study, all possible combinations of preprocessing tasks are evaluated on two different domains, namely e-mail and news, and in two different languages, namely Turkish and English. Their experiments showed that the impact of stemming, tokenization and lowercase conversion were overall positive on the classification accuracy. On the contrary, the impact of the stop word removal task was negative on the accuracy of classification system. The use of stop-word removal, stemming on spam email filtering, are analyzed by [PERSON] _et al._[PERSON] _et al._ (2015). It is concluded that performance of SVM is surprisingly better without using stemming and stop-word removal. However, some stop-words are rare in spam messages, and they should not be removed from the feature list in spite of being semantically void. [PERSON] _et al._[PERSON] _et al._ (2015) introduced the Thai document categorization framework which focused on the comparison of several feature representation schemes, including Boolean, Term Frequency (TF), Term Frequency inverse Document Frequency (TFiDF), Term Frequency Collection (TFC), Length Term Collection (LTC), Entropy, and Term Frequency Relevance Frequency (TF-RF). Tokenization, stop word removal and stemming were used as preprocessing tasks and chi-square as feature selection method. Three classification techniques are used, namely, naive bayes (NB), Decision Tree (DT), and support vector machine (SVM). The authors claimed that using TF-RF weighting with SVM classifier yielded the best performance with the F-measure equaling 95.9%. [PERSON] (2010,11) studied the impact of six commonly used feature selection techniques based on support vector machines (SVMs) on Arabic DC. Normalization and stop word removal techniques were used as preprocessing tasks in his experiments. He claimed that experiments proved that the stemming technique is not always effective for Arabic document categorization. His experimentsshowed that chi-square, the Ng-Goh-Low (NGL) coefficient, and the Galavotti-Sebastiani-Simi (GSS) coefficient significantly outperformed the other techniques with SVMs.