Arabic-SOS

Mohamed, Emad; Sayyed, Zeeshan Ali

doi:10.1145/3322905.3322927

Cited by 3 publications

(2 citation statements)

References 7 publications

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“…The topic models are then built through stems produced by the Arabic-SOS tools, where the Gradient Boosting Machine Learning algorithm is used for word segmentation, resulting in an accuracy rate of 98.8%. Next, the Mallet topic modeling toolkit (Mohamed and Sayyed 2019) within these models, topical words, having maximally higher frequency rates than others, are taken to be thematically correlating into clusters (themes). Through 50 and 100-topic modeling, we get a thematic distribution map, where the high-probability list co-concurrences (or near collocates) guide our close investigation of the distribution of such top words and reading of the selected texts to see how the surfacing topics cohere into meaningful patterns.…”

Section: Methodsmentioning

confidence: 99%

Racio-national Imaginary and Discursive Formation of Arabo-Islamic Identity in al-Manār and al-Risālah: A Topic Modeling Study

Mohamed,

Mohamed

2024

Journal of Cultural Analytics

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This article focuses on the dynamics of culture, language, and race as integral to the discourse on Pan-Islamist/Pan-Arabist national identification in Egypt during an era of drastic change in the Egyptian political and social spheres that set the stage for the current century that followed. Our approach draws on computational tool of topic modeling to probe relevant thematic discussions on the” conceptualization of race, language, culture, and identity by leading Arab-Muslim intelligentsia at a foundational moment that paved the way for Arab Nahḍah (modernity). Specifically, this analysis is meant to trace the intellectual development in the writings of Muḥammad Rashid Riḍā’s (1865-1935), which appeared in the magazine he edited, al-Manār (‘The Lighthouse’, 1898-1935), and those of Aḥmad Ḥasan al-Zayyāt’s (1885-1968), editor of al-Risālah (`The Messageʼ, 1933-1953), also a weekly magazine, both published in Cairo, Egypt. The study concludes that both figures sought to galvanize a largely hybridized Islamist/Arabist discourse as manifested in the clusteral paradigms of modelled topics.

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Section: Methodsmentioning

confidence: 99%

Racio-national Imaginary and Discursive Formation of Arabo-Islamic Identity in al-Manār and al-Risālah: A Topic Modeling Study

Mohamed,

Mohamed

2024

Journal of Cultural Analytics

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“…For the purposes of morphological segmentation, we use the Arabic-SOS package [17], which is specialized in Classical and pre-Modern Arabic. Arabic-SOS reports an accuracy of 99.5%, and we can confirm this very high accuracy on the corpus used in this article.…”

Section: A Note On the Arabic Languagementioning

confidence: 99%

Author verification of Nahj Al-Balagha

Sarwar¹,

Mohamed

2022

Digital Scholarship in the Humanities

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The primary purpose of this article is author verification of the Nahj Al-Balagha, a book attributed to Imam Ali and over which Sunni and Shi’i Muslims are proposing different theories. Given the morphologically complex nature of Arabic, we test whether morphological segmentation, applied to the book and works by the two authors suspected by Sunnis to have authored the texts, can be used for author verification of the Nahj Al-Balagha. Our findings indicate that morphological segmentation may lead to slightly better results than whole words and that regardless of the feature sets, the three sub-corpora cluster into three distinct groups using principal component analysis, hierarchical clustering, multi-dimensional scaling, and bootstrap consensus trees. Supervised classification methods such as Naive Bayes, Support Vector Machines, k Nearest Neighbours, Random Forests, AdaBoost, Bagging, and Decision Trees confirm the same results, which is a clear indication that (1) the book is internally consistent and can thus be attributed to a single person and (2) it was not authored by either of the suspected authors.

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Exploring Gender Differences in Fatwa through Machine Learning

Mohamed,

Sarwar

2024

Journal of Cultural Analytics

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This paper focuses on exploring the differences in inquiries made by men and women within a religious context. Additionally, we aim to ascertain whether it’s feasible to forecast the popularity of answers and the factors contributing to their popularity. To achieve this, we compile a new dataset comprising 40,000 question-answer pairs categorized by gender and popularity. These are sourced from online question-and-answer platforms. Our methodology involves comprehensive experimental analysis, utilizing advanced Arabic text preprocessing alongside machine learning algorithms. We concentrate on two primary objectives: predicting the gender of the questioner and forecasting the popularity of answers. Furthermore, we delve into thematic variations based on gender and address pivotal research queries that offer new perspectives within this domain. These include investigating the differences between questions posed by women versus men, exploring the potential for automated classification of queries by gender, predicting the popularity of fatwas, and identifying the contributing factors to their popularity. Our experimental findings demonstrate a 98% accuracy in gender prediction, precise predictions of popularity with minimal margin for error, and the identification of topics and their associations that are more inclined towards either men or women. We intend to share both the dataset and the source code openly with the research community.

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Arabic-SOS

Cited by 3 publications

References 7 publications

Racio-national Imaginary and Discursive Formation of Arabo-Islamic Identity in al-Manār and al-Risālah: A Topic Modeling Study

Racio-national Imaginary and Discursive Formation of Arabo-Islamic Identity in al-Manār and al-Risālah: A Topic Modeling Study

Author verification of Nahj Al-Balagha

Exploring Gender Differences in Fatwa through Machine Learning

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