Efficient Feature Representation Based on the Effect of Words Frequency for Arabic Documents Classification

Alhaj, Yousif A.; Wickramaarachchi, Wiraj Udara; Hussain, Akhtar; Al-qaness, Mohammed A. A.; Abdelaal, Hammam M.

doi:10.1145/3291842.3291900

Cited by 11 publications

(4 citation statements)

References 14 publications

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“…It is the process of removing words in the sentences that do not hold any important meaning, for example, for ‫ال‬ ‫ج‬ ‫ل‬ , which means "so." e researcher in Arabic mentions a list of words in [58,59].…”

Section: Stop Word Eliminationmentioning

confidence: 99%

Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

Muaad

Hanumanthappa

Guru

et al. 2022

Mathematical Problems in Engineering

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With the increasing number of online social posts, review comments, and digital documentations, the Arabic text classification (ATC) task has been hugely required for many spontaneous natural language processing (NLP) applications, especially within the coronavirus pandemics. The variations in the meaning of the same Arabic words could directly affect the performance of any AI-based framework. This work aims to identify the effectiveness of machine learning (ML) algorithms through preprocessing and representation techniques. This effectiveness is measured via different AI-based classification techniques. Basically, the ATC process is influenced by several factors such as stemming in preprocessing, method of feature extraction and selection, nature of datasets, and classification algorithm. To improve the overall classification performance, preprocessing techniques are mainly used to convert each Arabic word into its root and decrease the representation dimension among the datasets. Feature extraction and selection always play crucial roles to represent the Arabic text in a meaningful way and improve the classification accuracy rate. The selected classifiers in this study are performed based on various feature selection algorithms. The overall classification evaluation results are compared using different classifiers such as multinomial Naive Bayes (MNB), Bernoulli Naive Bayes (BNB), Stochastic Gradient Descent (SGD), Support Vector Classifier (SVC), Logistic Regression (LR), and Linear SVC. All of these AI classifiers are evaluated using five balanced and unbalanced benchmark datasets: BBC Arabic corpus, CNN Arabic corpus, Open-Source Arabic corpus (OSAc), ArCovidVac, and AlKhaleej. The evaluation results show that the classification performance strongly depends on the preprocessing technique, representation methods and classification technique, and the nature of datasets used. For the considered benchmark datasets, the linear SVC has outperformed other classifiers overall when prominent features are selected.

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Section: Stop Word Eliminationmentioning

confidence: 99%

Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

Muaad

Hanumanthappa

Guru

et al. 2022

Mathematical Problems in Engineering

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“…The words and the frequency counts of these vectors. 10 Reference 11 offered a text classification ensemble approach, in which BOW was utilized for the extraction of features, and used the Naive Bayes classifier, linear discriminant analysis, and support vector machine; the results were promising.…”

Section: Literature Reviewmentioning

confidence: 99%

A novel ensemble approach for heterogeneous data with active learning

Salama

Abdelkader

Abdelwahab

2022

International Journal of Engineering Business Management

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At present, millions of internet users are contributing a huge amount of data. This data is extremely heterogeneous, and so, it is hard to analyze and derive information from this data that is considered an indispensable source for decision-makers. Due to this massive growth, the classification of data and analysis has become an important research subject. Extracting information from this data has become a necessity. As a result, it was necessary to process these enormous volumes of data to uncover hidden information and therefore improve data analysis and, in turn, classification accuracy. In this paper, firstly, we focus on developing an ensemble machine-learning model based on active learning which identifies the most effective feature extraction strategy for heterogeneous data analysis, and compare it with traditional machine-learning algorithms. Secondly, we evaluate the proposed model during the experiments; five heterogeneous datasets from various domains were used, such as a Health Care Reform dataset, Sander Frandsen dataset, Financial Phrase Bank dataset, SMS Spam Collection dataset, and Textbook sales dataset. According to the results, the novel approach for data analysis performed better than conventional methods. Finally, the study’s findings confirmed the validity of the suggested technique, meeting the study’s goal of using ensemble methods with active learning to raise the model’s overall accuracy for effectively classifying and analyzing heterogeneous data, reducing the time and money spent training the model, and delivering superior analysis performance as well as insights into other elements of extracting information from heterogeneous data.

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“…The outcomes demonstrated that the SVM classifier gathered with ARLStem stemmer outperforms other classifiers when increasing the features. Moreover, in [49], the authors studied the effects of stop word removal in several classifiers and feature extractions using the CNN public dataset. Chi2 was applied as a feature selection method.…”

Section: Literature Reviewmentioning

confidence: 99%

“…Secondly, normalization transforms the characters into a standard format, remove (all non-Arabic characters, diacritics, numbers, and punctuation). Then, 1057 stop words lists were prepared to be removed from all the documents [49]. Finally, stemming was applied by reducing the word into their root/stem [50] using a novel stemmer [51].…”

Section: Preprocessingmentioning

confidence: 99%

A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

et al. 2022

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We propose a novel text classification model, which aims to improve the performance of Arabic text classification using machine learning techniques. One of the effective solutions in Arabic text classification is to find the suitable feature selection method with an optimal number of features alongside the classifier. Although several text classification methods have been proposed for the Arabic language using different techniques, such as feature selection methods, an ensemble of classifiers, and discriminative features, choosing the optimal method becomes an NP-hard problem considering the huge search space. Therefore, we propose a method, called Optimal Configuration Determination for Arabic text Classification (OCATC), which utilized the Particle Swarm Optimization (PSO) algorithm to find the optimal solution (configuration) from this space. The proposed OCATC method extracts and converts the features from the textual documents into a numerical vector using the Term Frequency-Inverse Document Frequency (TF–IDF) approach. Finally, the PSO selects the best architecture from a set of classifiers to feature selection methods with an optimal number of features. Extensive experiments were carried out to evaluate the performance of the OCATC method using six datasets, including five publicly available datasets and our proposed dataset. The results obtained demonstrate the superiority of OCATC over individual classifiers and other state-of-the-art methods.

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Efficient Feature Representation Based on the Effect of Words Frequency for Arabic Documents Classification

Cited by 11 publications

References 14 publications

Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

Arabic Document Classification: Performance Investigation of Preprocessing and Representation Techniques

A novel ensemble approach for heterogeneous data with active learning

A Novel Text Classification Technique Using Improved Particle Swarm Optimization: A Case Study of Arabic Language

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