Arabic Sentiment Analysis on Chewing Khat Leaves using Machine Learning and Ensemble Methods

Yafooz, Wael M. S.; Hizam, E. A.; Alromema, Waseem

doi:10.48084/etasr.4026

Cited by 16 publications

(8 citation statements)

References 22 publications

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“…Data pre-processing is a technique used to prepare data for sentiment classification [78,79]. The preparation involves cleaning, formatting and sorting the tweets that have been collected from Twitter to be saved in a dataset ready for analysis [45].…”

Section: Data Pre-processingmentioning

confidence: 99%

Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review

Alqurashi

2023

Eng. Technol. Appl. Sci. Res.

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Social media platforms have a huge impact on our daily lives. They have succeeded in attracting many people to spend time communicating and expressing themselves. Twitter is a social media platform that could be considered as a source of public opinion about products, services, and events. Sentiment analysis is the art of studying public feelings about certain topics, which may be positive, negative, or neutral. This paper provides a systematic review of Arabic tweet sentiment analysis on papers published from 2012 to 2021 in digital libraries including IEEE Explorer, Science Direct, Springer Link, and Google Scholar. The main aim of this systematic review is to investigate the trends in the topics reported and to highlight potential new research lines. To achieve that, three main stages were implemented: planning, conducting, and reporting the review. Our findings suggest the need for an open-source large Arabic tweet dataset that can be used by researchers. Also, it was found that researchers have used various classification techniques, which led to different results.

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Section: Data Pre-processingmentioning

confidence: 99%

Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review

Alqurashi

2023

Eng. Technol. Appl. Sci. Res.

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“…The following algorithms were selected and tested to build a model according to relevant studies conducted on Arabic language models [15,17]: Naïve Bayes classifier, Logistic Regression (LR), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), Random Forest (RF), and Decision Tree (DT).…”

Section: ) Algorithms Selectionmentioning

confidence: 99%

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

Baran,

Alzughaybi,

Bajafar

et al. 2023

Eng. Technol. Appl. Sci. Res.

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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.

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“…Like other languages, the Arabic language has many local dialects that people use instead of standard Arabic to express their ideas and opinions. Therefore, many researchers have studied ASA for their local dialects, such as the Moroccan [23], Algerian [4], Saudi [24], Jordanian [25], Tunisian [26], Egyptian [27], Iraqi [28], and Yemeni [29] dialects. Opinions and reviews in Google Play and the app store have motivated many researchers to analyse users' opinions of the different mobile applications available for download on these platforms [3,5,30,31].…”

Section: Literature Reviewmentioning

confidence: 99%

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

Hadwan¹,

Al-Hagery²,

Al-Sarem³

et al. 2022

Computers, Materials &Amp; Continua

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Different types of pandemics that have appeared from time to time have changed many aspects of daily life. Some governments encourage their citizens to use certain applications to help control the spread of disease and to deliver other services during lockdown. The Saudi government has launched several mobile apps to control the pandemic and have made these apps available through Google Play and the app store. A huge number of reviews are written daily by users to express their opinions, which include significant information to improve these applications. The manual processing and extracting of information from users' reviews is an extremely difficult and time-consuming task. Therefore, the use of intelligent methods is necessary to analyse users' reviews and extract issues that can help in improving these apps. This research aims to support the efforts made by the Saudi government for its citizens and residents by analysing the opinions of people in Saudi Arabia that can be found as reviews on Google Play and the app store using sentiment analysis and machine learning methods. To the best of our knowledge, this is the first study to explore users' opinions about governmental apps in Saudi Arabia. The findings of this analysis will help government officers make the right decisions to improve the quality of the provided services and help application developers improve these applications by fixing potential issues that cannot be identified during application testing phases. A new dataset used for this research includes 8000 user reviews gathered from social media, Google Play and the app store. Different methods are applied to the dataset, and the results show that the k nearest neighbourhood (KNN) method generates the highest accuracy compared to other implemented methods.

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Arabic Sentiment Analysis on Chewing Khat Leaves using Machine Learning and Ensemble Methods

Cited by 16 publications

References 22 publications

Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review

Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review

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

Arabic Sentiment Analysis of Users’ Opinions of Governmental Mobile Applications

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