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Gilbert Badaro

Gilbert Badaro has 8 papers in our library, in 2014–2023, most of them on Sentiment & emotion, Semitic languages, English; often with Hazem Hajj, Wassim El-Hajj, Ramy Baly.

8 papers in our library · 2014–2023

OpenAlex · ORCID · h-index 13 · 27 works · 929 citations OpenAlex, 2026-10-14

📄 8 works in our library · 🔗 Cited 136 times in our library · ⭐ Paper Stars ranking: #2989

The profile is the library's match of the name; two people with one name are not always told apart.

Elsewhere: Google Scholar · Semantic Scholar · dblp · ACL Anthology

 Affiliations

🎓American University of BeirutBeirut · Lebanon2013–2021 · 17 papersEducation 2013–2020 · Doctor of Philosophy, Electrical & Computer Engineering🎓EURECOMValbonne · France2021–2023 · 4 papersEmployment since 2021 · PostDoc Fellow, Data Science Department

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

 Fields, topics and concepts

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

📚 Shelves 8The field of science this work belongs to (OpenAlex fields).Computer Science · 8 worksArtificial Intelligence · 8 worksArts and Humanities · 7 worksLanguage and Linguistics · 7 worksPsychology · 4 worksExperimental and Cognitive Psychology · 4 worksComputer Vision and Pattern Recognition · 1 workSocial Psychology · 1 work

🧭 Topics 4Research subjects and languages the work is about.Sentiment & emotion · 7 worksSemitic languages · 6 worksLanguage models & pretraining · 1 workEnglish · 2 works

 Works by year

2020s

20231 paper

2010s

20182 papers20172 papers20152 papers20141 paper

 Papers

The most cited first (OpenAlex and our library).

  1. A Large Scale Arabic Sentiment Lexicon for Arabic Opinion Mining (2014 · 232 citations)
    Badaro and colleagues tackle the lack of a large, public Arabic sentiment lexicon that fits Arabic's rich morphology.
  2. 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…
  3. Transformers for Tabular Data Representation: A Survey of Models and Applications (2023 · 104 citations)
    Transformer language models have changed how free text is encoded, and a growing body of work adapts them to tables, which hold a large amount of knowledge.
  4. EMA at SemEval-2018 Task 1: Emotion Mining for Arabic (2018 · 73 citations)
    The paper describes EMA, a system built by a team at the American University of Beirut for the Arabic track of SemEval-2018 Task 1 (Affect in Tweets).
  5. A Characterization Study of Arabic Twitter Data with a Benchmarking for State-of-the-Art Opinion Mining Models (2017 · 67 citations)
    The paper looks at why sentiment analysis of Arabic tweets is hard and how well leading English opinion-mining models work when moved to Arabic.
  6. EmoWordNet: Automatic Expansion of Emotion Lexicon Using English WordNet (2018 · 43 citations)
    Emotion lexicons for English are much smaller than sentiment lexicons. For example, DepecheMood has about 37K entries, while SentiWordNet has about 150K. The authors grow DepecheMood into a larger resource called EmoWordNet without any manual annotation. Because DepecheMood lemmas are already aligned with English…
  7. 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:
  8. A Light Lexicon-based Mobile Application for Sentiment Mining of Arabic Tweets (2015 · 32 citations)
    The paper addresses sentiment analysis of Arabic tweets on a phone. Full Arabic NLP toolkits such as MADAMIRA cannot run on the device without a server, and server calls cost energy and response time. The authors replace heavy morphological processing with a sentiment lexicon stored locally. The lexicon is ArSenL…

 Co-authors here

11 co-authors with a page, the most papers together first.

Venues

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

 Cited by

Papers of our library cite their works 136 times (of the 149,439 papers whose bibliographies we read); 66 citing papers with a page here →.

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