A comparative study for Arabic text classification algorithms based on stop words elimination

Al-Shargabi, Bassam; Al-Romimah, Waseem; Olayah, Fekry

doi:10.1145/1980822.1980833

Cited by 36 publications

(19 citation statements)

References 3 publications

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“…In this step, the most popular statistical classification and machine learning techniques such as NB [34,35], KNN [36,37], and SVM [11,38] are suggested to study the influence of preprocessing on the Arabic DC system. The VSM that contains the selected features and their corresponding weights in each document of the training dataset are used to train the classification model.…”

Section: Document Categorizationmentioning

confidence: 99%

The Effect of Preprocessing on Arabic Document Categorization

et al. 2016

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Abstract:Preprocessing is one of the main components in a conventional document categorization (DC) framework. This paper aims to highlight the effect of preprocessing tasks on the efficiency of the Arabic DC system. In this study, three classification techniques are used, namely, naive Bayes (NB), k-nearest neighbor (KNN), and support vector machine (SVM). Experimental analysis on Arabic datasets reveals that preprocessing techniques have a significant impact on the classification accuracy, especially with complicated morphological structure of the Arabic language. Choosing appropriate combinations of preprocessing tasks provides significant improvement on the accuracy of document categorization depending on the feature size and classification techniques. Findings of this study show that the SVM technique has outperformed the KNN and NB techniques. The SVM technique achieved 96.74% micro-F1 value by using the combination of normalization and stemming as preprocessing tasks.

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Section: Document Categorizationmentioning

confidence: 99%

The Effect of Preprocessing on Arabic Document Categorization

et al. 2016

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“…Al-Shargabi compares three techniques for Arabic text classification based on stop words elimination [14]. These techniques are Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO), Naï ve Bayesian (NB), and J48 [6].The results of accuracy using these techniques achieved 94.8%, 89.42% and 85.07% respectively.…”

Section: Related Workmentioning

confidence: 99%

Arabic Text Categorization Using Mixed Words

Hussein¹,

Mousa²,

Sallam³

2016

IJITCS

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Abstract-There is a tremendous number of Arabic text documents available online that is growing every day. Thus, categorizing these documents becomes very important. In this paper, an approach is proposed to enhance the accuracy of the Arabic text categorization. It is based on a new features representation technique that uses a mixture of a bag of words (BOW) and two adjacent words with different proportions. It also introduces a new features selection technique depends on Term Frequency (TF) and uses Frequency Ratio Accumulation Method (FRAM) as a classifier. Experiments are performed without both of normalization and stemming, with one of them, and with both of them. In addition, three data sets of different categories have been collected from online Arabic documents for evaluating the proposed approach. The highest accuracy obtained is 98.61% by the use of normalization.

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“…Classification [21], [22], [23], [24], [25], [26], [27], [28], [29], [30], [31], [32], [33] , [34], [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [18], [48], [49], [50] 31(29%) Stemming and Lemmatization [51], [52], [53], [54], [55], [4], [56], [57], [58], [59], [60], [61] 12(11%) Information Retrieval [9], [62], [63], [64], [65], [66], [67], …”

Section: Techniquementioning

confidence: 99%

Arabic Text Mining a Systematic Review of the Published Literature 2002-2014

AlMahmoud

Al-Razgan

2015

2015 International Conference on Cloud Computing (ICCC)

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Text Mining is a set of techniques that analyzes large masses of data, extract relations that are unknown beforehand, and provide solutions to help decision-making. Text mining had been used extensively to analyze English text. However, text mining has only been used recently in analyzing Arabic text. As a result the objective of this paper is to present the current state of Arabic text mining. A systematic review has been performed to collect the papers published on the analysis of Arabic text mining. More than one hundred papers were used in our review from different reliable sources, and then they were classified according to their specific domain, and classified again according to the specific techniques used. This paper also provides quantitative analysis of publications according to publication type, year, category, and contributors.

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A comparative study for Arabic text classification algorithms based on stop words elimination

Cited by 36 publications

References 3 publications

The Effect of Preprocessing on Arabic Document Categorization

The Effect of Preprocessing on Arabic Document Categorization

Arabic Text Categorization Using Mixed Words

Arabic Text Mining a Systematic Review of the Published Literature 2002-2014

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