A novel methodology for Arabic news classification

Alfonse, Marco; Gawich, Mariam

doi:10.1002/widm.1440

Cited by 4 publications

(3 citation statements)

References 22 publications

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“…An innovative methodology [9] for the automatic categorization of Arabic news articles is discussed in this article. The technique uses machine learning classifiers such as Logistic Regression, Naive Bayes, Random Forest, XG Boosting, K-Nearest Neighbors, Decision Tree, Stochastic Gradient Descent, and Multi-Layer Perceptron to extract features from the dataset and make predictions.…”

Section: Related Workmentioning

confidence: 99%

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Unifying Sentence Transformer Embedding and Softmax Voting Ensemble for Accurate News Category Prediction

2023

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The study focuses on news category prediction and investigates the performance of sentence embedding of four transformer models (BERT, RoBERTa, MPNet, and T5) and their variants as feature vectors when combined with Softmax and Random Forest using two accessible news datasets from Kaggle. The data are stratified into train and test sets to ensure equal representation of each category. Word embeddings are generated using transformer models, with the last hidden layer selected as the embedding. Mean pooling calculates a single vector representation called sentence embedding, capturing the overall meaning of the news article. The performance of Softmax and Random Forest, as well as the soft voting of both, is evaluated using evaluation measures such as accuracy, F1 score, precision, and recall. The study also contributes by evaluating the performance of Softmax and Random Forest individually. The macro-average F1 score is calculated to compare the performance of different transformer embeddings in the same experimental settings. The experiments reveal that MPNet versions v1 and v3 achieve the highest F1 score of 97.7% when combined with Random Forest, while T5 Large embedding achieves the highest F1 score of 98.2% when used with Softmax regression. MPNet v1 performs exceptionally well when used in the voting classifier, obtaining an impressive F1 score of 98.6%. In conclusion, the experiments validate the superiority of certain transformer models, such as MPNet v1, MPNet v3, and DistilRoBERTa, when used to calculate sentence embeddings within the Random Forest framework. The results also highlight the promising performance of T5 Large and RoBERTa Large in voting of Softmax regression and Random Forest. The voting classifier, employing transformer embeddings and ensemble learning techniques, consistently outperforms other baselines and individual algorithms. These findings emphasize the effectiveness of the voting classifier with transformer embeddings in achieving accurate and reliable predictions for news category classification tasks.

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Section: Related Workmentioning

confidence: 99%

“…What this article adds is a mechanism for automating the categorization of Arabic news articles that may be used with different datasets and languages. There are other example of Arabic news exist in literature, e.g., [9][10][11].…”

Section: Related Workmentioning

confidence: 99%

Unifying Sentence Transformer Embedding and Softmax Voting Ensemble for Accurate News Category Prediction

2023

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“…TC is a machine learning challenge that tries to classify new written content into a conceptual group from a predetermined classification collection [1]. It is crucial in a variety of applications, including sentiment analysis [2,3], spam email filtering [4,5], hate speech detection [6], text summarization [7], website classification [8], authorship attribution [9], information retrieval [10], medical diagnostics [11], emotion detection on smart phones [12], online recommendations [13], fake news detection [14,15], crypto-ransomware early detection [16], semantic similarity detection [17], part-of-speech tagging [18], news classification [19], and tweet classification [20].…”

Section: Introductionmentioning

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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Performance Benchmark of Machine Learning-Based Methodology for Swahili News Article Categorization

Little

Roy

Hamoud

2022

2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)

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A novel methodology for Arabic news classification

Cited by 4 publications

References 22 publications

Unifying Sentence Transformer Embedding and Softmax Voting Ensemble for Accurate News Category Prediction

Unifying Sentence Transformer Embedding and Softmax Voting Ensemble for Accurate News Category Prediction

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

Performance Benchmark of Machine Learning-Based Methodology for Swahili News Article Categorization

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