Etiqa'a: An Android Mobile Application for Monitoring Teen's Private Messages on WhatsApp to Detect Harmful/Inappropriate Words in Arabic using Machine Learning

被引：0

作者：

Baran, Faiza Mohammed Usman ^{[1
]}

Alzughaybi, Lama Saleh Abdullah ^{[1
]}

Bajafar, Manar Ahmed Saeed ^{[1
]}

Alsaedi, Maram Nasser Muslih ^{[1
]}

Serdar, Thraa Freed Hassan ^{[1
]}

Mirza, Olfat Meraj Nawab ^{[1
]}

机构：

[1] Umm Al Qura Univ, Dept Comp Sci, Mecca, Saudi Arabia

来源：

ENGINEERING TECHNOLOGY & APPLIED SCIENCE RESEARCH | 2023年 / 13卷 / 06期

关键词：

machine learning; Artificial Intelligence (AI); Natural Language Processing (NLP); WhatsApp; private message monitoring; Arabic text classification; message classification; SENTIMENT ANALYSIS;

D O I：

暂无

中图分类号：

T [工业技术];

学科分类号：

08 ;

摘要：

In today's world, social networks, such as WhatsApp, have become essential to daily life. An increasing number of Arab children use WhatsApp to communicate with others on a local and global scale, which has led to several negative consequences in their lives, including those associated with being bullied and harassed online. This study presents Etiqa'a, an application aiming to minimize risks and keep threats against minors from becoming a reality. Etiqa'a scans received WhatsApp messages which are then analyzed, and classified using a Logistic Regression (LR) machine learning model. The test results showed an accuracy of 81% in classifying messages as appropriate or inappropriate based on the text of the message. In the case of the latter, the application sends a detailed alert to parents.

引用

页码：12012 / 12019

页数：8