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Alaa Khaddaj

Alaa Khaddaj has 3 papers in our library, in 2018–2019, most of them on Semitic languages, Sentiment & emotion; often with Hazem Hajj, Wassim El-Hajj, Gilbert Badaro.

3 papers in our library · 2018–2019

OpenAlex · h-index 7 · 14 works · 209 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 41 times in our library · ⭐ Paper Stars ranking: #6258

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

🎓Massachusetts Institute of TechnologyCambridge · United States2020–2025 · 5 paperscurrent🎓American University of BeirutBeirut · Lebanon2018–2020 · 4 papers

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 7The field of science this work belongs to (OpenAlex fields).Computer Science · 3 worksArtificial Intelligence · 3 worksPsychology · 2 worksExperimental and Cognitive Psychology · 2 worksSocial Psychology · 1 workArts and Humanities · 1 workLanguage and Linguistics · 1 work

🧭 Topics 2Research subjects and languages the work is about.Semitic languages · 3 worksSentiment & emotion · 2 works

 Works by year

2010s

20192 papers20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. 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).
  2. ArSentD-LEV: A Multi-Topic Corpus for Target-based Sentiment Analysis in Arabic Levantine Tweets (2019 · 61 citations)
    Most Arabic sentiment datasets assign only a single polarity label to a whole text, often on a coarse three-way scale.
  3. Improved Generalization of Arabic Text Classifiers (2019 · 10 citations)
    The paper addresses a gap in Arabic NLP: classifiers trained on one domain of text lose accuracy on another, and transfer techniques that are common for English had barely been tried for Arabic.

 Co-authors here

4 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 41 times (of the 149,439 papers whose bibliographies we read); 29 citing papers with a page here →.

Updated

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