Vectorization and Sentiment Analysis of Arabizi Text

Vectorization and Sentiment Analysis of Arabizi Text | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Vectorization and Sentiment Analysis of Arabizi Text noha youssef, Sama Gouda, Farida Madkour, Mona Ibrahim This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8509085/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In recent years, a new form of Arabic has emerged to facilitate communication between younger generations, particularly with the advent of social media platforms. Globalization was one of the primary factors that increased the importance of the English language, particularly with the widespread adoption of technology and the dominance of various technological devices and platforms \cite{HAL}. This new form of Arabic is 'Arabizi, a portmanteau of Araby-Englizi, meaning Arabic-English, is a digital trend in texting Non-Standard Arabic using Latin script \cite{taha01}. The intensified use of Arabizi has given rise to a plethora of new research concerns about how to interpret this type of language using various machine learning approaches. Consequently, Natural Language Processing (NLP) might aid in deriving substantial insights, allowing sentiment analysis. This study will review the various approaches presented in the literature to address this topic. Then we tested a set of machine learning models, deep learning models, and tested out ensembles made with both. The results were that a fine-tuned Support Vector Machine (SVC) produced the best results with an accuracy of 0.63 and F1 score of 0.59. Arabizi Franco-Arabic Natural Language Processing Machine Learning Sentiment Analysis Transliteration Embeddings Deep Learning Ensemble Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8509085","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":576203313,"identity":"4715319d-4c0c-4131-869b-3716de0c4338","order_by":0,"name":"noha 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