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%.

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%.

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.

scholarly journals The Evaluation of Accuracy Performance in an Enhanced Embedded Feature Selection for Unstructured Text Classification

2020 ◽  
pp. 3397-3407
Author(s):  
Nur Syafiqah Mohd Nafis ◽  
Suryanti Awang
Keyword(s):  
Feature Selection ◽  
Text Classification ◽  
Training Dataset ◽  
Recursive Feature Elimination ◽  
High Dimensional ◽  
Significant Feature ◽  
Support Vector ◽  
Svm Classifier ◽  
Text Documents ◽  
Text Document

Text documents are unstructured and high dimensional. Effective feature selection is required to select the most important and significant feature from the sparse feature space. Thus, this paper proposed an embedded feature selection technique based on Term Frequency-Inverse Document Frequency (TF-IDF) and Support Vector Machine-Recursive Feature Elimination (SVM-RFE) for unstructured and high dimensional text classificationhis technique has the ability to measure the feature’s importance in a high-dimensional text document. In addition, it aims to increase the efficiency of the feature selection. Hence, obtaining a promising text classification accuracy. TF-IDF act as a filter approach which measures features importance of the text documents at the first stage. SVM-RFE utilized a backward feature elimination scheme to recursively remove insignificant features from the filtered feature subsets at the second stage. This research executes sets of experiments using a text document retrieved from a benchmark repository comprising a collection of Twitter posts. Pre-processing processes are applied to extract relevant features. After that, the pre-processed features are divided into training and testing datasets. Next, feature selection is implemented on the training dataset by calculating the TF-IDF score for each feature. SVM-RFE is applied for feature ranking as the next feature selection step. Only top-rank features will be selected for text classification using the SVM classifier. Based on the experiments, it shows that the proposed technique able to achieve 98% accuracy that outperformed other existing techniques. In conclusion, the proposed technique able to select the significant features in the unstructured and high dimensional text document.

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 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.

An Experimental Study for the Effect of Stop Words Elimination for Arabic Text Classification Algorithms

2014 ◽  
pp. 184-190
Author(s):  
Bassam Al-Shargabi ◽  
Fekry Olayah ◽  
Waseem AL Romimah
Keyword(s):  
Experimental Study ◽  
Support Vector Machine ◽  
Text Classification ◽  
Error Rate ◽  
Support Vector ◽  
Arabic Text ◽  
Sequential Minimal Optimization ◽  
Split Method ◽  
Arabic Text Classification ◽  
Fold Cross Validation

In this paper, an experimental study was conducted on three techniques for Arabic text classification. These techniques are Support Vector Machine (SVM) with Sequential Minimal Optimization (SMO), Naïve Bayesian (NB), and J48. The paper assesses the accuracy for each classifier and determines which classifier is more accurate for Arabic text classification based on stop words elimination. The accuracy for each classifier is measured by Percentage split method (holdout), and K-fold cross validation methods, along with the time needed to classify Arabic text. The results show that the SMO classifier achieves the highest accuracy and the lowest error rate, and shows that the time needed to build the SMO model is much lower compared to other classification techniques.

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.

scholarly journals Comparison of Random Forest and Support Vector Machine for Indonesian Tweet Complaint Classification

2019 ◽  
pp. 202-207 ◽  
Author(s):  
Desi Ramayanti
Keyword(s):  
Support Vector Machine ◽  
Random Forest ◽  
Text Classification ◽  
Research Area ◽  
Computational Time ◽  
Support Vector ◽  
Svm Classifier ◽  
Text Documents ◽  
Case Organization

In digital business, the managerial commonly need to process text so that it can be used to support decision-making. The number of text documents contained ideas and opinions is progressing and challenging to understand one by one. Whereas if the data are processed and correctly rendered using machine learning, it can present a general overview of a particular case, organization, or object quickly. Numerous researches have been accomplished in this research area, nevertheless, most of the studies concentrated on English text classification. Every language has various techniques or methods to classify text depending on the characteristics of its grammar. The result of classification among languages may be different even though it used the same algorithm. Given the greatness of text classification, text classification algorithms that can be implemented is the support vector machine (SVM) and Random Forest (RF). Based on the background above, this research is aimed to find out the performance of support vector machine algorithm and random forest in classification of Indonesian text. 1. Result of SVM classifier with cross validation k-10 is derived the best accuracy with value 0.9648, however, it spends computational time as long as 40.118 second. Then, result of RF classifier with values, i.e. 'bootstrap': False, 'min_samples_leaf': 1, 'n_estimators': 10, 'min_samples_split': 3, 'criterion': 'entropy', 'max_features': 3, 'max_depth': None is achieved accuracy is 0.9561 and computational time 109.399 second.

scholarly journals Arabic Text Classification: A Review

2019 ◽  
Vol 13 (5) ◽  
pp. 88
Author(s):  
Adel Hamdan Mohammad
Keyword(s):  
Text Classification ◽  
Arabic Language ◽  
Support Vector ◽  
K Nearest Neighbors ◽  
Electronic Documents ◽  
Vector Machines ◽  
C4.5 Decision Tree ◽  
On Line ◽  
Arabic Text Classification ◽  
Bayesian Support

Text classification is an important topic. The number of electronic documents available on line is massive. Text classification aims to classify documents into a set of predefined categories.&nbsp; Number of researches conducted on English dataset is great in comparison with number of researches done using Arabic dataset. This research could be considered as reference for most researchers who deal with Arabic dataset. This research used the most well-known algorithms used in text classification with Arabic dataset. Besides that, dataset used in this research is large enough in comparison with most dataset for Arabic language used in other researches. In addition, this research used different selections and weighting methods for documents. I expect that all researchers who would write researches using Arabic dataset will find this work helpful. Algorithms used in this research are na&iuml;ve Bayesian, support vector machines, artificial neural networks, k- nearest neighbors, C4.5 decision tree and rocchio classifier.

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>

Sign in / Sign up

Export Citation Format

Share Document