Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review

Alqurashi, Tahani

doi:10.48084/etasr.5662

Cited by 11 publications

(5 citation statements)

References 73 publications

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“…Arabic text is written from right to left, unlike Latin, and is differentiated by the lack of upper-or lower-case letters. Only 28 letters that make up Arabic language alphabet, 25 of which are consonants and just 3 are vowels [33]. Arabic script also uses markings of diacritical as short-vowels in addition to these vocal parts.…”

Section: Extracting Dialectal Arabic Featuresmentioning

confidence: 99%

Sentiment Analysis Methods for Arabic Content on Social Media: A Systematic Review

AlMotairi,

Hadwan

2024

ISI

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The topics of sentiment analysis either written or auditory texts are among important research areas in artificial intelligence (AI). The researchers in Natural Processing language (NLP) concerned more about the development of sentiment analysis methods and applications. For the available literature, the English Language has most research studies for the sentiment analysis. Arabic language is a membership in an entirely distinct language family than English explains why it needs the researchers to explore it from the scratch. Consequently, languages other than Arabic are more closely connected to one another than Arabic is. The Indo-European languages are utterly alien to the grammar, syntax, pronunciation, and lexicon. As a result, Arabic sentiment analysis has recently attracted the attention of the scientific researchers. This is due to the huge number of ideas and thoughts that are posted daily by social media users around the world. Manual processing of such huge data to obtain valuable information is an impossible task. The aim of this systematic review is to present a comprehensive review the major contributions in the field of Arabic sentiment analysis (ASA). The previous studies were primarily focused on dealing with certain sentiment analysis tasks, according to a comprehensive analysis of the accessible literature. The approaches found in the literature for ASA is classified into three main groups: (i) supervised, (ii) unsupervised, and (iii) hybrid. The literature's primary points include the fact that sentiment analysis in Arabic is difficult due to the language's complexity and wide variety of local dialects. These research findings, while intriguing, were not all in agreement. This difference is mostly attributable to the method chosen, the job being examined, besides the peculiarities and nuances of the Arabic diversity being studied. The evaluation of the literature revealed that, Naï ve bayes (NB), K-Nearest Neighbour (KNN) and Support Victor Machine (SVM) are among the most popular classifiers applied to ASA. The lack of trusted Arabic data sets to allow the researchers examine the proposed ASA methods is among the main issues not yet solved. Therefore, this research can help the researchers to get updated about the literature related to Arabic sentiment analysis datasets and existed methods and techniques.

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Section: Extracting Dialectal Arabic Featuresmentioning

confidence: 99%

Sentiment Analysis Methods for Arabic Content on Social Media: A Systematic Review

AlMotairi,

Hadwan

2024

ISI

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“…Preprocessing was used to prepare the data for training and classification [15], which consisted of cleaning the data, ANLP, and special feature extraction.…”

Section: B Data Preprocessingmentioning

confidence: 99%

“…After cleaning the data, data processing for the Arabic language was involved, which included tokenization, normalization, stemming or lemmatizing, and stop-word removal [15]. Stop words are a group of frequently used words that include determiners, conjunctions, and prepositions.…”

Section: ) Data Processing Using Anlpmentioning

confidence: 99%

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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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“…Arabic Sentiment Analysis (ASA) approaches can be classified into three main categories: corpus-based (supervised, unsupervised, and hybrid learning), lexicon-based (unsupervised learning), and hybrid-based [15]. Corpus-based techniques seek to leverage a large amount of Twitter data available to construct machine learning models in order to accurately classify sentiments associated with individual tweets.…”

Section: Introductionmentioning

confidence: 99%

Unveiling Sentiments: A Comprehensive Analysis of Arabic Hajj-Related Tweets from 2017–2022 Utilizing Advanced AI Models

Alghamdi

2024

BDCC

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Sentiment analysis plays a crucial role in understanding public opinion and social media trends. It involves analyzing the emotional tone and polarity of a given text. When applied to Arabic text, this task becomes particularly challenging due to the language’s complex morphology, right-to-left script, and intricate nuances in expressing emotions. Social media has emerged as a powerful platform for individuals to express their sentiments, especially regarding religious and cultural events. Consequently, studying sentiment analysis in the context of Hajj has become a captivating subject. This research paper presents a comprehensive sentiment analysis of tweets discussing the annual Hajj pilgrimage over a six-year period. By employing a combination of machine learning and deep learning models, this study successfully conducted sentiment analysis on a sizable dataset consisting of Arabic tweets. The process involves pre-processing, feature extraction, and sentiment classification. The objective was to uncover the prevailing sentiments associated with Hajj over different years, before, during, and after each Hajj event. Importantly, the results presented in this study highlight that BERT, an advanced transformer-based model, outperformed other models in accurately classifying sentiment. This underscores its effectiveness in capturing the complexities inherent in Arabic text.

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Arabic Sentiment Analysis for Twitter Data: A Systematic Literature Review

Cited by 11 publications

References 73 publications

Sentiment Analysis Methods for Arabic Content on Social Media: A Systematic Review

Sentiment Analysis Methods for Arabic Content on Social Media: A Systematic 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

Unveiling Sentiments: A Comprehensive Analysis of Arabic Hajj-Related Tweets from 2017–2022 Utilizing Advanced AI Models

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