Arabic News Classification Based on the Country of Origin Using Machine Learning and Deep Learning Techniques

被引：1

作者：

Zamzami, Nuha ^{[1
]}

Himdi, Hanen ^{[1
]}

Sabbeh, Sahar F. ^{[2
,3
]}

机构：

[1] Univ Jeddah, Coll Comp Sci & Engn, Dept Comp Sci & Artificial Intelligence, Jeddah 23218, Saudi Arabia

[2] Univ Jeddah, Coll Comp Sci & Engn, Dept Informat Syst & Technol, Jeddah 23218, Saudi Arabia

[3] Benha Univ, Fac Comp & Artificial Intelligence, Banha 13518, Egypt

来源：

APPLIED SCIENCES-BASEL | 2023年 / 13卷 / 12期

关键词：

news source; news country of origin; classification; machine learning; deep learning; TEXT CLASSIFICATION;

D O I：

10.3390/app13127074

中图分类号：

O6 [化学];

学科分类号：

0703 ;

摘要：

With the rise of Arabic news articles published daily, people are becoming increasingly concerned about following the news from reliable sources, especially regarding events that impact their country. To assess a news article's significance to the user, it is essential to identify the article's country of origin. This paper proposes several classification models that categorize Arabic news articles based on their country of origin. The models were developed using comprehensive machine learning and deep learning techniques with several feature training methods. The results show the ability of our model to classify news articles based on their country of origin, with close accuracy between machine learning and deep learning techniques of up to 94%.

引用

页数：22