Do Words with Certain Part of Speech Tags Improve the Performance of Arabic Text Classification?

Abstract

Feature extraction - the process of choosing feature types that can represent and discriminate between dataset topics - is one of the critical steps in text classification and varies with the language of the texts. Different feature types have been proposed for Arabic text classification, ranging from features based on word orthography (single word and character and word N-grams) to features based on linguistic analysis (roots, stems). To the best of our knowledge, little attention has been paid to investigating the performance of Arabic text classification when Part of Speech (POS) tagging information is used to extract features. In this study, we used a corpus comprising 4900 newspaper texts distributed evenly over seven topics to investigate the effect of using POS tag distribution and words that belong to certain POS tags on Arabic text classification, namely nouns, verbs and adjectives. For feature selection, feature representation and text classification we used Chi-squared, Log-Weighted Term Frequency Inverse Document Frequency with Cosine Normalization (LTC) and support vector machine (SVM) respectively. We used four metrics, namely accuracy, precision, recall and F-measure to measure classification performance. Experiment data suggest that the words achieved the best classification performance when the number of features was low; however, the classification performance can be marginally increased when nouns, verbs and adjectives are used as features, given that the number of features is increased.