Request to Join SIGARAB and Seek Guidance on Arabic NLP Research

[mohammedratib007]

Dear SIGARAB Community,

I hope this message finds you well.

My name is Mohammed Ratib, and I am writing to express my interest in joining the SIGARAB community and engaging actively in its research and scholarly activities. My academic background combines language studies, teaching experience, and a growing specialization in data science, machine learning, and Natural Language Processing.

I hold a Bachelor’s degree in English Language and Education, and I have several years of experience as an English language instructor. Alongside my teaching career, I have been building a strong technical foundation in Python, statistics, machine learning, and NLP through extensive hands-on training and applied projects. My research interests are particularly focused on Arabic NLP, with emphasis on language understanding, text processing, and diacritization.

I am the author of the research papers entitled “A Critical Survey on Arabic Named Entity Recognition and Diacritization Systems,” and "Predicting Cryptocurrency Prices During Periods of Confict: A Comparative Sentiment Analysis Using SVM, CNN‑LSTM, and Pysentimento" conducted under the supervision of Prof. Sameh Al-Ansary, Head of the Phonetics Department, Alexandria University. This work examines the challenges of Arabic diacritization, including ambiguity arising from the omission of diacritic marks, the role of grammatical knowledge and context in restoring meaning, and the linguistic importance of diacritics in disambiguation and pronunciation. The study also provides a detailed linguistic analysis of Arabic diacritic types, including short vowels, nunation, and gemination, and highlights their critical role in Arabic language technologies.

As for "Predicting Cryptocurrency Prices During Periods of Confict: A Comparative Sentiment Analysis Using SVM, CNN‑LSTM, and Pysentimento", This study explores how conflict‑driven public sentiment influences cryptocurrency price movements. It presents a comparative analysis between traditional machine‑learning models (SVM), deep learning architectures (CNN‑LSTM), and lexicon‑based sentiment analysis tools (PySentimiento), highlighting their respective strengths in capturing sentiment signals from textual data and modeling market behavior during periods of instability.

At this stage of my academic journey, I am eager to further develop my research profile and would greatly appreciate guidance from the SIGARAB community regarding:

1-The procedure for joining SIGARAB and participating in its academic and research initiatives

2-How to effectively initiate, refine, and publish new research ideas in Arabic NLP

3-Recommended research directions, datasets, benchmarks, and tools for early-stage researchers

Best practices for communicating with SIGARAB members for mentorship, collaboration, and scholarly exchange

I am highly motivated to learn, contribute, and grow within the Arabic NLP research ecosystem, and I deeply appreciate SIGARAB’s role in fostering collaboration and advancing Arabic AI research.

Thank you very much for your time and consideration.

Kind regards,

Mohammed Ratib

Department of English Language and Translation

Faculty of Arts

Alexandria University

Alexandria, Arab Republic of Egypt

[mohame...@alexu.edu.eg](mailto:mohame...@alexu.edu.eg)