The impact of indexing approaches on Arabic text classification

Abstract

This paper investigates the impact of using different indexing approaches (full-word, stem, and root) when classifying Arabic text. In this study, the naïve Bayes classifier is used to construct the multinomial classification models and is evaluated using stratified k-fold cross-validation ( k ranges from 2 to 10). It is also uses a corpus that consists of 1000 normalized Arabic documents. The results of one experiment in this study show that significant accuracy improvements have occurred when the full-word form is used in most k-folds. Further experiments show that the classifier has achieved the highest accuracy in the eight-fold by using 7/8–1/8 train–test ratio, despite the indexing approach being used. The overall results of this study show that the classifier has achieved the maximum micro-average accuracy 99.36%, either by using the full-word form or the stem form. This proves that the stem is a better choice to use when classifying Arabic text, because it makes the corpus dataset smaller and this will enhance both the processing time and storage utilization, and achieve the highest level of accuracy.

ASA: A framework for Arabic sentiment analysis

“…The same results were obtained by Al-Badarneh et al [27] and Mustafa et al [43], which demonstrated that stem is a better choice to use in classifying Arabic text. Our obtained result is consistent with the finding of Liu and Zhang [45] which showed that the pre-processing steps like stemming improves SA accuracy.…”

“…Therefore, various research works may differ about the efficiency of the same stemmers. In addition, while some articles showed that Khoja stemmer is less efficient [20], other articles confirmed that Khoja has a good performance [26,27]. For this reason, we considered Khoja in our research.…”

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“…Moreover, Hmeidi et al [12] studied the influence of raw text, khoja root-based stemmer and light stemming of Arabic text documents based on standard classifiers, such as NB, SVM, KNN, J48 and Decision Table classifiers. The results exhibited that the SVM and NB classifiers with light stemming provides better classification accuracy than other classifiers.The same conclusion was drawn up by Al-Badarneh [13] and Ayedh et al [14] by using various pre-processing methods. Additionally, Al-Molegi et al [15] and Khreisat [16] have proposed an approach to classify Arabic text documents based on the combination of N-grams with some similarity measures, including Manhattan, Euclidean distances and Dice.…”

Classification of colloquial Arabic tweets in real-time to detect high-risk floods

“…The authors have assessed their framework against [13] and [14]; the outcome of this comparison demonstrated the effectiveness of their proposed framework, it outperformed other approaches in the Normalized Discounted Cumulative Gain (NDCG) and precision evaluation measures. Further, the work of Al-Badarneh et al [15] investigated the impact of using different indexing techniques (full-word, stem, and root) when classifying Arabic text. It concludes that using 'fullword' or 'stem' outperforms 'root' when applied with the Naïve Bayes (NB) classifier.…”