Issues of Dialectal Saudi Twitter Corpus

Alruily, Meshrif

doi:10.34028/iajit/17/3/10

Cited by 10 publications

(11 citation statements)

References 12 publications

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“…Several corpora were also created solely for SD language. One such corpus is the Dialectal Saudi Twitter Corpus (Saudi Dialect) [4], which is an SD corpus containing 207,452 tweets generated by 101 Saudi Twitter users and collected in 2017. Moreover, the SaUdi corpus for NLP Applications and Resources (SUAR) [10] is another SD corpus; it consists of 104,079 words from different online resources (blogs, forums, Instagram, Twitter, WhatsApp, YouTube).…”

Section: Literature Reviewmentioning

confidence: 99%

“…CA is the official language used in the Quran and during the medieval period, MSA is the official language used in the modern period and by news outlets, and DA is the spoken language that is used in daily life and differs from one country/city to another. Moreover, there are many subcategories under these Arabic languages, and thus, Arabic is considered to be highly inflectional and to have a very complex morphology [4]. Therefore, the development of large Arabic corpora has been the focus of many researchers in recent years.…”

Section: Introductionmentioning

confidence: 99%

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An Incremental Approach to Corpus Design and Construction: Application to a Large Contemporary Saudi Corpus

et al. 2021

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Due to the rapid developments in technology and the sudden expansion of social media use, Dialect Arabic has become an important source of data that needs to be addressed when building Arabic corpora. In this paper, thirty-three Arabic corpora are surveyed to show that despite all of the developments in the literature, Saudi dialect (SD) corpora still need further expansion. This paper contributes to the literature on SD corpora by creating the largest Saudi corpus -the King Saud University Saudi Corpus (KSUSC)with +1B total words, including +119M SD words. The KSUSC not only is the newest and largest SD corpus but is also diverse, covering 26 domains in text collected from five different sources. This paper also contributes to the literature by developing a new incremental preprocessing system that is used to create relevant lexicons that are then used to clean and normalize the collected data. This incremental system is scalable and can be adapted for different resources and dialects. Moreover, the collection process for building the KSUSC is discussed in detail, and the challenges in collecting SD text with respect to each platform are highlighted. By the end of this paper, different design criteria are proposed and used with the KSUSC to conclude that the resulting corpus can be of great benefit to researchers who are interested in integrating the corpus with their own work or using its resulting lexicons with Saudi-based NLP tasks.

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Section: Literature Reviewmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Incremental Approach to Corpus Design and Construction: Application to a Large Contemporary Saudi Corpus

et al. 2021

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“…By investigation the sentences that are found in the two corpora, it is obvious that "qSdk" conveys three procedural meanings in SA: asking for clarification, correction, and making irony. For instance, "qṣdk" in (7) helps the listener to infer that the speaker finds the word "tutorial" difficult to be understood. The speaker uses the DM "qSdk" which conveys the pragmatic meaning of asking for clarification to ask the listener if the word "tutorial" means "makeup" or something else.…”

Section: B the Pragmatic Functions And Procedural Meanings Of The Saudi Dm "Qsdk"mentioning

confidence: 99%

The Meaning and Functions of qSdk and yʕny as Discourse Markers in Saudi Arabic

Alshammary

2021

tpls

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The current study aims at investigating two discourse markers that are used in Saudi Arabic, “qSdk and yʕny”, against Schourup’s characteristics of discourse markers which are connectivity, optionality, and non-truth conditionality. Additionally, this study investigates the pragmatic uses and procedural meanings of those discourse markers using Blakemore’s procedural meaning and relevance theory as a framework. By examining two discourse markers that received less attention in other studies, the current study builds on previous literature in this field. Regarding methodology, the current study is a corpus-based study in which two corpora containing texts written in Saudi Arabic are used to extract data and evidence. The study concludes that “qSdk and yʕny” behave as discourse markers by being optional, connecting two segments together, and having no influence on the truth condition of the sentence in which they are used. The discourse marker “qSdk” serves three procedural meanings: asking for clarification, correction and making irony whereas “yʕny” serves the procedural meanings of clarification and asking for clarification. Furthermore, the use of these discourse markers makes the sentence more relevant to listeners as they need less cognitive effect to derive the pragmatic meaning of the sentence.

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“…Due to that, a huge amount of data on social media websites and microblogs, such as Twitter and Facebook, are being added every day (Altaher, 2017). A study has shown that Saudi Arabia has the highest annual growth rate of social media users around the world (Alruily, 2020). With Twitter users posting about 500 million tweets per day, over 30% of these tweets are from Saudi Arabia (Alruily, 2020).…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of cyberbullying and threatening in Saudi tweets using machine learning

Al-Ghamdi¹

2021

Int. j. adv. appl. sci.

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Social media has become a major factor in people's lives, which affects their communication and psychological state. The widespread use of social media has formed new types of violence, such as cyberbullying. Manual detection and reporting of violent texts in social media applications are challenging due to the increasing number of social media users and the huge amounts of generated data. Automatic detection of violent texts is language-dependent, and it requires an efficient detection approach, which considers the unique features and structures of a specific language or dialect. Only a few studies have focused on the automatic detection and classification of violent texts in the Arabic Language. This paper aims to build a two-level classifier model for classifying Arabic violent texts. The first level classifies text into violent and non-violent. The second level classifies violent text into either cyberbullying or threatening. The dataset used to build the classifier models is collected from Twitter, using specific keywords and trending hashtags in Saudi Arabia. Supervised machine learning is used to build two classifier models, using two different algorithms, which are Support Vector Machine (SVM), and Naive Bayes (NB). Both models are trained in different experimental settings of varying the feature extraction method and whether stop-word removal is applied or not. The performances of the proposed SVM-based and NB-based models have been compared. The SVM-based model outperforms the NB-based model with F1 scores of 76.06%, and 89.18%, and accuracy scores of 73.35% and 87.79% for the first and second levels of classification, respectively.

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Issues of Dialectal Saudi Twitter Corpus

Cited by 10 publications

References 12 publications

An Incremental Approach to Corpus Design and Construction: Application to a Large Contemporary Saudi Corpus

An Incremental Approach to Corpus Design and Construction: Application to a Large Contemporary Saudi Corpus

The Meaning and Functions of qSdk and yʕny as Discourse Markers in Saudi Arabic

Automatic detection of cyberbullying and threatening in Saudi tweets using machine learning

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