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Amira Barhoumi

Amira Barhoumi has 3 papers in our library, in 2018–2020, most of them on Sentiment & emotion, Semitic languages, Evaluation & benchmarks; often with Nathalie Camelin, Yannick Estève, Chafik Aloulou.

3 papers in our library · 2018–2020

OpenAlex · h-index 4 · 12 works · 46 citations OpenAlex, 2026-10-14

📄 3 works in our library · 🔗 Cited 1 time in our library · ⭐ Paper Stars ranking: #11483

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

🔬Laboratoire d’Informatique de l’Université du MansLe Mans · France2017–2022 · 8 paperscurrent🎓Le Mans UniversitéLe Mans · France2019–2022 · 2 paperscurrent

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 4The field of science this work belongs to (OpenAlex fields).Artificial Intelligence · 3 worksArts and Humanities · 3 worksComputer Science · 3 worksLanguage and Linguistics · 3 works

🧭 Topics 3Research subjects and languages the work is about.Semitic languages · 3 worksSentiment & emotion · 3 worksEvaluation & benchmarks · 1 work

 Works by year

2020s

20201 paper

2010s

20191 paper20181 paper

 Papers

The most cited first (OpenAlex and our library).

  1. Toward Qualitative Evaluation of Embeddings for Arabic Sentiment Analysis (2020 · 5 citations)
    Arabic is agglutinative and morphologically rich: one surface word can carry several clitics, and missing diacritics hide meanings with different polarity.
  2. Des représentations continues de mots pour l'analyse d'opinions en arabe: une étude qualitative (Word embeddings for Arabic sentiment analysis : a qualitative study) (2018 · 2 citations)
    The paper looks at polarity classification of Arabic book reviews with neural models. Arabic has few openly available sentiment resources, so the authors ask how far freely released word embeddings can take a system that has no expert lexicons. They build two classifiers: a Kim-style CNN with filter windows of 3, 5…
  3. Plongements lexicaux spécifiques à la langue arabe : application à l'analyse d'opinions (Arabic-specific embedddings : application in Sentiment Analysis) (2019)
    The paper asks whether Arabic sentiment classification improves when word embeddings respect the language's agglutinative, morphologically rich structure.

 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 1 time (of the 149,439 papers whose bibliographies we read); 1 citing paper with a page here →.

Updated

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