scholarly journals A Survey of Arabic Text Classification Models

2018 ◽  
Vol 8 (6) ◽  
pp. 4352 ◽  
Author(s):  
Ahed M. F. Al-Sbou
Keyword(s):  
Language Processing ◽  
Text Classification ◽  
Arabic Language ◽  
Arabic Text ◽  
Classification Models ◽  
Natural Languages ◽  
Text Organization ◽  
Arabic Text Classification ◽  
Arabic Language Processing

<p>There is a huge content of Arabic text available over online that requires an organization of these texts. As result, here are many applications of natural languages processing (NLP) that concerns with text organization. One of the is text classification (TC). TC helps to make dealing with unorganized text. However, it is easier to classify them into suitable class or labels. This paper is a survey of Arabic text classification. Also, it presents comparison among different methods in the classification of Arabic texts, where Arabic text is represented a complex text due to its vocabularies. Arabic language is one of the richest languages in the world, where it has many linguistic bases. The researche in Arabic language processing is very few compared to English. As a result, these problems represent challenges in the classification, and organization of specific Arabic text. Text classification (TC) helps to access the most documents, or information that has already classified into specific classes, or categories to one or more classes or categories. In addition, classification of documents facilitate search engine to decrease the amount of document to, and then to become easier to search and matching with queries.</p>

scholarly journals A survey of arabic text classification models

2019 ◽  
Vol 8 (1) ◽  
pp. 25
Author(s):  
Ahed M. F. Al Sbou
Keyword(s):  
Language Processing ◽  
Text Classification ◽  
Arabic Language ◽  
Arabic Text ◽  
Classification Models ◽  
Natural Languages ◽  
Text Organization ◽  
Arabic Text Classification ◽  
Arabic Language Processing

<span>There is a huge content of Arabic text available over online that requires an organization of these texts. As result, here are many applications of natural languages processing (NLP) that concerns with text organization. One of the is text classification (TC). TC helps to make dealing with unorganized text. However, it is easier to classify them into suitable class or labels. This paper is a survey of Arabic text classification. Also, it presents comparison among different methods in the classification of Arabic texts, where Arabic text is represented a complex text due to its vocabularies. Arabic language is one of the richest languages in the world, where it has many linguistic bases. The research in Arabic language processing is very few compared to English. As a result, these problems represent challenges in the classification, and organization of specific Arabic text. Text classification (TC) helps to access the most documents, or information that has already classified into specific classes, or categories to one or more classes or categories. In addition, classification of documents facilitate search engine to decrease the amount of document to, and then to become easier to search and matching with queries.</span>

Improving Arabic Text Classification Using P-Stemmer

2020 ◽  
Vol 13 ◽  
Author(s):  
Tarek Kanan ◽  
Bilal Hawashin ◽  
Shadi Alzubi ◽  
Eyad Almaita ◽  
Ahmad Alkhatib ◽  
...  
Keyword(s):  
Language Processing ◽  
Text Classification ◽  
Text Categorization ◽  
English Language ◽  
Arabic Language ◽  
Online News ◽  
Support Vector ◽  
Arabic Text ◽  
Fast Learning ◽  
Arabic Text Classification

Introduction: Stemming is an important preprocessing step in text classification, and could contribute in increasing text classification accuracy. Although many works proposed stemmers for English language, few stemmers were proposed for Arabic text. Arabic language has gained increasing attention in the previous decades and the need is vital to further improve Arabic text classification. Method: This work combined the use of the recently proposed P-Stemmer with various classifiers to find the optimal classifier for the P-stemmer in term of Arabic text classification. As part of this work, a synthesized dataset was collected. Result: The previous experiments show that the use of P-Stemmer has a positive effect on classification. The degree of improvement was classifier-dependent, which is reasonable as classifiers vary in their methodologies. Moreover, the experiments show that the best classifier with the P-Stemmer was NB. This is an interesting result as this classifier is wellknown for its fast learning and classification time. Discussion: First, the continuous improvement of the P-Stemmer by more optimization steps is necessary to further improve the Arabic text categorization. This can be made by combining more classifiers with the stemmer, by optimizing the other natural language processing steps, and by improving the set of stemming rules. Second, the lack of sufficient Arabic datasets, especially large ones, is still an issue. Conclusion: In this work, an improved P-Stemmer was proposed by combining its use with various classifiers. In order to evaluate its performance, and due to the lack of Arabic datasets, a novel Arabic dataset was synthesized from various online news pages. Next, the P-Stemmer was combined with Naïve Bayes, Random Forest, Support Vector Machines, KNearest Neighbor, and K-Star.

The Effect of Stemming on Arabic Text Classification

2011 ◽  
Vol 1 (3) ◽  
pp. 54-70 ◽  
Author(s):  
Abdullah Wahbeh ◽  
Mohammed Al-Kabi ◽  
Qasem Al-Radaideh ◽  
Emad Al-Shawakfa ◽  
Izzat Alsmadi
Keyword(s):  
Text Classification ◽  
Digital Libraries ◽  
Arabic Language ◽  
Support Vector ◽  
Svm Classifier ◽  
Arabic Text ◽  
Text Documents ◽  
Information Retrieval Systems ◽  
Arabic Text Classification ◽  
The Web

The information world is rich of documents in different formats or applications, such as databases, digital libraries, and the Web. Text classification is used for aiding search functionality offered by search engines and information retrieval systems to deal with the large number of documents on the web. Many research papers, conducted within the field of text classification, were applied to English, Dutch, Chinese, and other languages, whereas fewer were applied to Arabic language. This paper addresses the issue of automatic classification or classification of Arabic text documents. It applies text classification to Arabic language text documents using stemming as part of the preprocessing steps. Results have showed that applying text classification without using stemming; the support vector machine (SVM) classifier has achieved the highest classification accuracy using the two test modes with 87.79% and 88.54%. On the other hand, stemming has negatively affected the accuracy, where the SVM accuracy using the two test modes dropped down to 84.49% and 86.35%.

scholarly journals Semantic Ontology-Based Approach to Enhance Arabic Text Classification

2019 ◽  
Vol 3 (4) ◽  
pp. 53
Author(s):  
Ahmad Hawalah
Keyword(s):  
Text Classification ◽  
Arabic Language ◽  
Arabic Text ◽  
Inverse Document Frequency ◽  
Classification Techniques ◽  
Topic Discovery ◽  
Document Frequency ◽  
The Rich ◽  
Semantic Ontology ◽  
Arabic Text Classification

Text classification is a process of classifying textual contents to a set of predefined classes and categories. As enormous numbers of documents and contextual contents are introduced every day on the Internet, it becomes essential to use text classification techniques for different purposes such as enhancing search retrieval and recommendation systems. A lot of work has been done to study different aspects of English text classification techniques. However, little attention has been devoted to study Arabic text classification due to the difficulty of processing Arabic language. Consequently, in this paper, we propose an enhanced Arabic topic-discovery architecture (EATA) that can use ontology to provide an effective Arabic topic classification mechanism. We have introduced a semantic enhancement model to improve Arabic text classification and the topic discovery technique by utilizing the rich semantic information in Arabic ontology. We rely in this study on the vector space model (term frequency-inverse document frequency (TF-IDF)) as well as the cosine similarity approach to classify new Arabic textual documents.

The effects of Pre-Processing Techniques on Arabic Text Classification

2021 ◽  
Vol 10 (1) ◽  
pp. 41-48
Keyword(s):  
Text Classification ◽  
Arabic Language ◽  
Machine Learning Algorithms ◽  
Arabic Text ◽  
Content Filtering ◽  
Text Data ◽  
Arabic Text Classification ◽  
Processing Techniques ◽  
The Impact ◽  
E Mail

In the last two decades, the amount of available Arabic text data on the World Wide Web is dramatically growing, making it the fourth most used language on the web. Accordingly, the demand for efficient Arabic text classification is increasing, especially for web page content filtering, information retrieval, and e-mail spam detection. Several Machine Learning algorithms have been implemented to classify Arabic documents. However, the results achieved are not comparable with those obtained in other languages such as English, primarily when using preprocessing techniques that do not take into consideration the Arabic language features. This paper investigates the impact of wisely selected preprocessing techniques on the efficiency of different text classification algorithms. The effects of stop words removal, stemming, lemmatization, and all possible combinations are examined. The reported results (+10.75% to +28.73%) prove the effectiveness of using these techniques either individually or in combination.

The Effect of Stemming on Arabic Text Classification

2013 ◽  
pp. 207-225 ◽  
Author(s):  
Abdullah Wahbeh ◽  
Mohammed Al-Kabi ◽  
Qasem Al-Radaideh ◽  
Emad Al-Shawakfa ◽  
Izzat Alsmadi
Keyword(s):  
Text Classification ◽  
Digital Libraries ◽  
Arabic Language ◽  
Support Vector ◽  
Svm Classifier ◽  
Arabic Text ◽  
Text Documents ◽  
Information Retrieval Systems ◽  
Arabic Text Classification ◽  
The Web

The information world is rich of documents in different formats or applications, such as databases, digital libraries, and the Web. Text classification is used for aiding search functionality offered by search engines and information retrieval systems to deal with the large number of documents on the web. Many research papers, conducted within the field of text classification, were applied to English, Dutch, Chinese, and other languages, whereas fewer were applied to Arabic language. This paper addresses the issue of automatic classification or classification of Arabic text documents. It applies text classification to Arabic language text documents using stemming as part of the preprocessing steps. Results have showed that applying text classification without using stemming; the support vector machine (SVM) classifier has achieved the highest classification accuracy using the two test modes with 87.79% and 88.54%. On the other hand, stemming has negatively affected the accuracy, where the SVM accuracy using the two test modes dropped down to 84.49% and 86.35%.

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