Effects of Light Stemming on Feature Extraction and Selection for Arabic Documents Classification

Abstract

This chapter aims to study the effects of the light stemming technique on feature extraction where Bag of Words (BoW) and Term frequency- Inverse Documents (TF-IDF) are employed for Arabic document classification. Moreover, feature selection methods such as Chi-square (Chi2), Information gain (IG), and singular value decomposition (SVD) are used to select the most relevant features. K-nearest Neighbor (kNN), Logistic Regression (LR), and Support Vector Machine (SVM) classifiers are used to build the classification model. Experiment are conducted using a public data collected from Arab websites, namely, BBC Arabic dataset. Experiment results show that SVM outperforms LR and KNN. Furthermore, BoW outperforms TF-IDF without using a stemming technique. Using a Robust Arabic Light Stemmer (ARLStem) as our main light stemmer shows a positive effect when combined with TF-IDF over the baseline. In the experiment where Chi2 is used as the feature selection technique, SVM resulted in 0.9568% F1-micro using BoW to extract the features from the dataset where 5000 relevant features were selected. In the experiment where IG is used as the feature selection method, SVM achieved 0.9588% F1-micro with BoW and 4000 selected features. Finally in the experiment where SVD is used as the feature selection technique, SVM reached 0.9569% F1-micro when using BoW and 5000 relevant feature were selected. The aforementioned experiments report the best results achieved where stemming is not employed.