Preprocessing Arabic text on social media

Hegazi, Mohamed Osman; Al-Dossari, Yasser; Al-Yahy, Abdullah; Al-Sumari, Abdulaziz Al-Sumari; Hilal, Anwer Mustafa

doi:10.1016/j.heliyon.2021.e06191

Cited by 52 publications

(28 citation statements)

References 26 publications

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“…Arabic text has several characteristics that make it more challenging for NLP systems such as diacritics and mixture of dialect, modern, and classical texts. These challenges can be observed obviously in social media texts [46]. Preprocessing the text before feeding it to machine learning algorithms is important and can improve the accuracy of these models sharply [47].…”

Section: Preprocessingmentioning

confidence: 99%

Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

Alturayeif

Luqman

2021

Applied Sciences

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The outbreak of coronavirus disease (COVID-19) has affected almost all of the countries of the world, and has had significant social and psychological effects on the population. Nowadays, social media platforms are being used for emotional self-expression towards current events, including the COVID-19 pandemic. The study of people’s emotions in social media is vital to understand the effect of this pandemic on mental health, in order to protect societies. This work aims to investigate to what extent deep learning models can assist in understanding society’s attitude in social media toward COVID-19 pandemic. We employ two transformer-based models for fine-grained sentiment detection of Arabic tweets, considering that more than one emotion can co-exist in the same tweet. We also show how the textual representation of emojis can boost the performance of sentiment analysis. In addition, we propose a dynamically weighted loss function (DWLF) to handle the issue of imbalanced datasets. The proposed approach has been evaluated on two datasets and the attained results demonstrate that the proposed BERT-based models with emojis replacement and DWLF technique can improve the sentiment detection of multi-dialect Arabic tweets with an F1-Micro score of 0.72.

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Section: Preprocessingmentioning

confidence: 99%

Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

Alturayeif

Luqman

2021

Applied Sciences

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“…Arabic is a Semitic language that is strongly tied to Islam and Muslim culture, and it is the language of the Quran, used by all Muslims (over 1.62 billion people) [23]. It is also the mother tongue of over 422 million people [24]. There are 28 alphabets in this language, and lines are expressed from right to left [25].…”

Section: Arabic Languagementioning

confidence: 99%

“…The data obtained via the internet is unstructured and must be preprocessed before being used in later stages [24], [27]. A great deal of work is required before preprocessing Arabic content on social media, since most of it will be informal (not standard) and may include dialects, misspellings, characters with diacritical marks, and elongations [12].…”

Section: Arabic Text Preprocessingmentioning

confidence: 99%

Detecting Arabic textual threats in social media using artificial intelligence: An overview

Elzayady

Mohamed

Badran

et al. 2022

IJEECS

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<span>Recent studies show that social media has become an integral part of everyone's daily routine. People often use it to convey their ideas, opinions, and critiques. Consequently, the increasing use of social media has motivated malicious users to misuse online social media anonymity. Thus, these users can exploit this advantage and engage in socially unacceptable behavior. The use of inappropriate language on social media is one of the greatest societal dangers that exist today. Therefore, there is a need to monitor and evaluate social media postings using automated methods and techniques. The majority of studies that deal with offensive language classification in texts have used English datasets. However, the enhancement of offensive language detection in Arabic has gotten less consideration. The Arabic language has different rules and structures. This article provides a thorough review of research studies that have made use of artificial intelligence (AI) for the identification of Arabic offensive language in various contexts.</span>

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“…Finally, the task of Arabic preprocessing has received attention in previous work [37][38] [18] including an approach presented by the authors [25]. Here we create two Sudanese Arabic sentiment datasets, one 2-Class and one 3-Class.…”

Section: Related Workmentioning

confidence: 99%

A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets

Mustafa¹,

Sutcliffe²,

Sun³

et al. 2022

Preprint

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Arabic sentiment analysis has become an important research field in recent years. Initially, work focused on Modern Standard Arabic (MSA), which is the most widely-used form. Since then, work has been carried out on several different dialects, including Egyptian, Levantine and Moroccan. Moreover, a number of datasets have been created to support such work. However, up until now, less work has been carried out on Sudanese Arabic, a dialect which has 32 million speakers. In this paper, two new publicly available datasets are introduced, the 2-Class Sudanese Sentiment Dataset (SudSenti2) and the 3-Class Sudanese Sentiment Dataset (SudSenti3). Furthermore, a CNN architecture, SCM, is proposed, comprising five CNN layers together with a novel pooling layer, MMA, to extract the best features. This SCM+MMA model is applied to SudSenti2 and SudSenti3 with accuracies of 92.75% and 84.39%. Next, the model is compared to other deep learning classifiers and shown to be superior on these new datasets. Finally, the proposed model is applied to the existing Saudi Sentiment Dataset and to the MSA Hotel Arabic Review Dataset with accuracies 85.55% and 90.01%.

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Preprocessing Arabic text on social media

Cited by 52 publications

References 26 publications

Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

Fine-Grained Sentiment Analysis of Arabic COVID-19 Tweets Using BERT-Based Transformers and Dynamically Weighted Loss Function

Detecting Arabic textual threats in social media using artificial intelligence: An overview

A Deep CNN Architecture with Novel Pooling Layer Applied to Two Sudanese Arabic Sentiment Datasets

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