Ahmad Al Sallab

Ahmad Al Sallab has 3 papers in our library, in 2014–2017, most of them on Semitic languages, Sentiment & emotion, Morphology; often with Gilbert Badaro, Hazem Hajj, Khaled Shaban.

3 papers in our library · 2014–2017

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Affiliations

From the bylines of their papers (OpenAlex, 2026-10-14), ORCID and Wikidata; institutions on papers are matched by machine.

Of its works in our library, with the number of works on each.

The most cited first (OpenAlex and our library).

Deep Learning Models for Sentiment Analysis in Arabic (2015 · 162 citations)
The paper addresses sentence-level sentiment classification (positive vs. negative) for Arabic. Rich morphology, ambiguity and many dialects make opinion mining in Arabic hard, and the authors present this as the first study of deep learning for the task in that language. They compare four architectures on sentences…
OMAM at SemEval-2017 Task 4: Evaluation of English State-of-the-Art Sentiment Analysis Models for Arabic and a New Topic-based Model (2017 · 33 citations)
The paper tackles sentiment analysis of Arabic tweets within SemEval-2017 Task 4. Rich morphology, dialect variation, misspellings and Twitter-specific tokens make this task harder than in English. The OMAM team built four systems:
Automatic Arabic diacritics restoration based on deep nets (2014 · 23 citations)
Modern Standard Arabic is usually written without diacritics, yet speech synthesis, translation and similar applications need them restored.

12 of 13 co-authors with a page, the most papers together first.

Where their works here were published, the most works first, with the editions of those that have a page.

Papers of our library cite their works 37 times (of the 149,439 papers whose bibliographies we read); 30 citing papers with a page here →.

Updated 31 October 2026