A survey of Arabic text classification approaches

Sayed, Mostafa; Salem, Rashed; Khder, Ayman E.

doi:10.1504/ijcat.2019.098601

Cited by 23 publications

(17 citation statements)

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“…A limited number of surveys are carried out on Arabic text classification techniques compared to English [29]. In addition, few studies have reviewed the approaches used for measuring Arabic text similarity [4,6].…”

Section: Related Workmentioning

confidence: 99%

Automatic Arabic Short Answers Scoring Using Longest Common Subsequence and Arabic WordNet

Abdeljaber

2021

IEEE Access

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The manual process of scoring short answers of Arabic essay questions is exhaustive, susceptible to error and consumes instructor's time and resources. This paper explores longest common subsequence (LCS) algorithm as a string-based text similarity measure for effectively scoring short answers of Arabic essay questions. To achieve this effectiveness, the longest common subsequence is modified by developing weight-based measurement techniques and implemented along with using Arabic WordNet for scoring Arabic short answers. The experiments conducted on a dataset of 330 students' answers reported Root Mean Square Error (RMSE) value of 0.81 and Pearson correlation r value of 0.94. Findings based on experiments have shown improvements in the accuracy of performance of the proposed approach compared to similar studies. Moreover, the statistical analysis has shown that the proposed method scores students' answers similar to that of human estimator.

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Section: Related Workmentioning

confidence: 99%

Automatic Arabic Short Answers Scoring Using Longest Common Subsequence and Arabic WordNet

Abdeljaber

2021

IEEE Access

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“…VSM is popularly applied for index terms extraction, documents indexing, and documents ranking [19]. Measuring text documents similarity is applied using similarity measures such as cosine similarity [30] [31] , Euclidian distance [32].…”

Section: B Vector Space Modelmentioning

confidence: 99%

A Proposed Model for Detecting Facebook News’ Credibility

Idrees¹,

Alsheref²,

ElSeddawy³

2019

IJACSA

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Social networks are currently one of the main News' sources for most of their users. Moreover, News channels also consider social networks as main channels not only for spreading the news but also for measuring the feedback from their followers. Facebook Followers can comment or react to the news, which represents the follower's feedback about this topic. Therefore, it is a fact that measuring the News' credibility is one of the important tasks that could control the propagation of the fake news as well as the number of News' followers. The proposed model in this research highlights the impact of the News' followers on detecting the News' polarity either it is fake or not. The proposed model focuses on applying an intelligent sentiment analysis using Vector Space Model (VSM) which is one of the most successful techniques on the users' comments and reactions through the emoji. Then the degree of credibility is determined according to the correlation coefficient. An experimental study was applied using Facebook News dataset, which included the News and the followers' feedbacks.

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“…In terms of Arabic text classification, several Machine Learning algorithms have been successfully implemented such as Naïve Bayes (NB), Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighbors (K-NN), Artificial Neural Network (ANN) [4]. However, for the Arabic language, the text classifier performance is not only influenced by the algorithm implemented; also, the nature of the language has a great impact on the developed classifier.…”

Section: Introductionmentioning

confidence: 99%

The effects of Pre-Processing Techniques on Arabic Text Classification

2021

IJATCSE

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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.

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A survey of Arabic text classification approaches

Cited by 23 publications

References 0 publications

Automatic Arabic Short Answers Scoring Using Longest Common Subsequence and Arabic WordNet

Automatic Arabic Short Answers Scoring Using Longest Common Subsequence and Arabic WordNet

A Proposed Model for Detecting Facebook News’ Credibility

The effects of Pre-Processing Techniques on Arabic Text Classification

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