A novel methodology for Arabic news classification

IF 6.4 2区计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery Pub Date : 2021-12-06 DOI:10.1002/widm.1440

Marco Alfonse, M. Gawich

引用次数: 4

Abstract

The automated news classification concerns the assignment of news to one or more predefined categories. The automated classified news helps the search engines to mine and categorize the type of news that the user asks for. Most of the researchers focused on the classification of English news and ignore the Arabic news due to the complexity of the Arabic morphology. This article presents a novel methodology to classify the Arabic news. It relies on the use of features extraction and the application of machine learning classifiers which are the Naive Bayes (NB), the Logistic Regression (LR), the Random Forest (RF), the Xtreme Gradient Boosting (XGB), the K‐Nearest Neighbors (KNN), the Stochastic Gradient Descent (SGD), the Decision Tree (DT), and the Multi‐Layer Perceptron (MLP). The methodology is applied to the Arabic news dataset provided by Mendeley. The accuracy of the classification is more than 95%.

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一种新的阿拉伯语新闻分类方法

自动新闻分类涉及将新闻分配到一个或多个预定义的类别。自动分类新闻帮助搜索引擎挖掘和分类用户所要求的新闻类型。由于阿拉伯语词法的复杂性，以往的研究大多集中在英语新闻的分类上，而忽略了阿拉伯语新闻的分类。本文提出了一种新的阿拉伯语新闻分类方法。它依赖于特征提取和机器学习分类器的应用，这些分类器是朴素贝叶斯(NB)、逻辑回归(LR)、随机森林(RF)、Xtreme梯度增强(XGB)、K近邻(KNN)、随机梯度下降(SGD)、决策树(DT)和多层感知器(MLP)。该方法应用于Mendeley提供的阿拉伯语新闻数据集。分类准确率在95%以上。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE-COMPUTER SCIENCE, THEORY & METHODS

CiteScore

22.70

自引率

2.60%

发文量

审稿时长

>12 weeks

期刊介绍： The goals of Wiley Interdisciplinary Reviews-Data Mining and Knowledge Discovery (WIREs DMKD） are multifaceted. Firstly, the journal aims to provide a comprehensive overview of the current state of data mining and knowledge discovery by featuring ongoing reviews authored by leading researchers. Secondly, it seeks to highlight the interdisciplinary nature of the field by presenting articles from diverse perspectives, covering various application areas such as technology, business, healthcare, education, government, society, and culture. Thirdly, WIREs DMKD endeavors to keep pace with the rapid advancements in data mining and knowledge discovery through regular content updates. Lastly, the journal strives to promote active engagement in the field by presenting its accomplishments and challenges in an accessible manner to a broad audience. The content of WIREs DMKD is intended to benefit upper-level undergraduate and postgraduate students, teaching and research professors in academic programs, as well as scientists and research managers in industry.